{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Linear Modeling in Python\n",
    "\n",
    "This notebook, which was created and presented by Skipper Sebold at the Scipy2012, introduces the use of pandas and the formula framework in statsmodels in the context of linear modeling.\n",
    "\n",
    "Adjusted by Thomas Haslwanter, April-2020\n",
    "**It is based heavily on Jonathan Taylor's [class notes that use R](http://www.stanford.edu/class/stats191/interactions.html)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.graphics.api import interaction_plot, abline_plot, qqplot\n",
    "from statsmodels.stats.api import anova_lm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Example 1: IT salary data\n",
    "\n",
    "In this example we will first establish a linear model, of salary as a function of experience, education, and management level.\n",
    "We will test for any interactions between these factors. Significant interactions will be included in the model.\n",
    "Finally, we will remove outliers, and plot the resulting fits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Get the data"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Outcome:    S, salaries for IT staff in a corporation\n",
    "Predictors: X, experience in years\n",
    "            M, managment, 2 levels, 0=non-management, 1=management\n",
    "            E, education, 3 levels, 1=Bachelor's, 2=Master's, 3=Ph.D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "url = 'http://stats191.stanford.edu/data/salary.table'\n",
    "salary_table = pd.read_table(url) \n",
    "#salary_table.to_csv('salary.table', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Inspect the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       S  X  E  M\n",
      "0  13876  1  1  1\n",
      "1  11608  1  3  0\n",
      "2  18701  1  3  1\n",
      "3  11283  1  2  0\n",
      "4  11767  1  3  0\n",
      "5  20872  2  2  1\n",
      "6  11772  2  2  0\n",
      "7  10535  2  1  0\n",
      "8  12195  2  3  0\n",
      "9  12313  3  2  0\n"
     ]
    }
   ],
   "source": [
    "print(salary_table.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "E = salary_table.E # Education\n",
    "M = salary_table.M # Management\n",
    "X = salary_table.X # Experience\n",
    "S = salary_table.S # Salary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's explore the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,8))\n",
    "ax = fig.add_subplot(111, xlabel='Experience', ylabel='Salary',\n",
    "            xlim=(0, 27), ylim=(9600, 28800))\n",
    "symbols = ['D', '^']\n",
    "man_label = [\"Non-Mgmt\", \"Mgmt\"]\n",
    "educ_label = [\"Bachelors\", \"Masters\", \"PhD\"]\n",
    "colors = ['r', 'g', 'blue']\n",
    "factor_groups = salary_table.groupby(['E','M'])\n",
    "for values, group in factor_groups:\n",
    "    i,j = values\n",
    "    label = \"%s - %s\" % (man_label[j], educ_label[i-1])\n",
    "    ax.scatter(group['X'], group['S'], marker=symbols[j], color=colors[i-1],\n",
    "               s=350, label=label)\n",
    "ax.legend(scatterpoints=1, markerscale=.7, labelspacing=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Define and Fit a Linear Model\n",
    "\n",
    "Fit a linear model\n",
    "\n",
    "$$S_i = \\beta_0 + \\beta_1X_i + \\beta_2E_{i2} + \\beta_3E_{i3} + \\beta_4M_i + \\epsilon_i$$\n",
    "\n",
    "where\n",
    "\n",
    "$$ E_{i2}=\\cases{1,&if $E_i=2$;\\cr 0,&otherwise. \\cr}$$ \n",
    "$$ E_{i3}=\\cases{1,&if $E_i=3$;\\cr 0,&otherwise. \\cr}$$ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "In the following, the model is defined using \"patsy\".\n",
    "\n",
    "- An intercept is automatically included.\n",
    "- C(variable) includes the variable automatically as categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      S   R-squared:                       0.957\n",
      "Model:                            OLS   Adj. R-squared:                  0.953\n",
      "Method:                 Least Squares   F-statistic:                     226.8\n",
      "Date:                Tue, 14 Apr 2020   Prob (F-statistic):           2.23e-27\n",
      "Time:                        22:11:01   Log-Likelihood:                -381.63\n",
      "No. Observations:                  46   AIC:                             773.3\n",
      "Df Residuals:                      41   BIC:                             782.4\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   8035.5976    386.689     20.781      0.000    7254.663    8816.532\n",
      "C(E)[T.2]   3144.0352    361.968      8.686      0.000    2413.025    3875.045\n",
      "C(E)[T.3]   2996.2103    411.753      7.277      0.000    2164.659    3827.762\n",
      "C(M)[T.1]   6883.5310    313.919     21.928      0.000    6249.559    7517.503\n",
      "X            546.1840     30.519     17.896      0.000     484.549     607.819\n",
      "==============================================================================\n",
      "Omnibus:                        2.293   Durbin-Watson:                   2.237\n",
      "Prob(Omnibus):                  0.318   Jarque-Bera (JB):                1.362\n",
      "Skew:                          -0.077   Prob(JB):                        0.506\n",
      "Kurtosis:                       2.171   Cond. No.                         33.5\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "formula = 'S ~ C(E) + C(M) + X'\n",
    "lm = ols(formula, salary_table).fit()\n",
    "print(lm.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "*Dep. Variable*\n",
    "    the variable to be fitted; here, the \"Salary\"\n",
    "\n",
    "*Model*\n",
    "    OLS = ordinary least squares\n",
    "\n",
    "*Df Residuals*\n",
    "    The number of observations, minus the number of parameters fitted.\n",
    "\n",
    "*Df model*\n",
    "    \"Degree of Freedom\" of the model, i.e. the dimensionnality of the subspace spanned by the model. This entails that the intercept is not counted.\n",
    "\n",
    "*R-squared*\n",
    "    Coefficient of Determination = (S0-Sm)/S0\n",
    "\n",
    "*Adj. R-squared*\n",
    "    The adjusted R2 coefficient, which takes into consideration the number of model paramters.\n",
    "\n",
    "*F-statistic*, and corresponding *Prob*\n",
    "    F-test on the regression model, if it is significantly different from the minimum model (i.e. a constant offset only)\n",
    "\n",
    "*Log-Likelihood*\n",
    "    Maximum log-likelihood value for the model.\n",
    "\n",
    "*AIC*\n",
    "    Aiken's Information Criterion, for the assessment of the model.\n",
    "\n",
    "*BIC*\n",
    "    Bayesian Information Criterion, for the assessment of the model.\n",
    "\n",
    "The values in the lowest box describe properties of the residuals (\"Skew\", \"Kurtosisi\"), as well as tests on the residuals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Inspect the Design Matrix, and demonstrate the Model Predictions\n",
    "\n",
    "Look at the design matrix created for us. Every results instance has a reference to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 1., 1.],\n",
       "       [1., 0., 1., 0., 1.],\n",
       "       [1., 0., 1., 1., 1.],\n",
       "       [1., 1., 0., 0., 1.],\n",
       "       [1., 0., 1., 0., 1.],\n",
       "       [1., 1., 0., 1., 2.],\n",
       "       [1., 1., 0., 0., 2.],\n",
       "       [1., 0., 0., 0., 2.],\n",
       "       [1., 0., 1., 0., 2.],\n",
       "       [1., 1., 0., 0., 3.]])"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.model.exog[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Since we initially passed in a DataFrame, we have a transformed DataFrame available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Intercept  C(E)[T.2]  C(E)[T.3]  C(M)[T.1]    X\n",
      "0        1.0        0.0        0.0        1.0  1.0\n",
      "1        1.0        0.0        1.0        0.0  1.0\n",
      "2        1.0        0.0        1.0        1.0  1.0\n",
      "3        1.0        1.0        0.0        0.0  1.0\n",
      "4        1.0        0.0        1.0        0.0  1.0\n",
      "5        1.0        1.0        0.0        1.0  2.0\n",
      "6        1.0        1.0        0.0        0.0  2.0\n",
      "7        1.0        0.0        0.0        0.0  2.0\n",
      "8        1.0        0.0        1.0        0.0  2.0\n",
      "9        1.0        1.0        0.0        0.0  3.0\n"
     ]
    }
   ],
   "source": [
    "print(lm.model.data.orig_exog.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "There is a reference to the original untouched data in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       S  X  E  M\n",
      "0  13876  1  1  1\n",
      "1  11608  1  3  0\n",
      "2  18701  1  3  1\n",
      "3  11283  1  2  0\n",
      "4  11767  1  3  0\n",
      "5  20872  2  2  1\n",
      "6  11772  2  2  0\n",
      "7  10535  2  1  0\n",
      "8  12195  2  3  0\n",
      "9  12313  3  2  0\n"
     ]
    }
   ],
   "source": [
    "print(lm.model.data.frame.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "If you use the formula interface, statsmodels remembers this transformation. Say you want to know the predicted salary for someone with 12 years experience and a Master's degree who is in a management position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24617.372072\n",
       "dtype: float64"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.predict(pd.DataFrame({'X' : [12], 'M' : [1], 'E' : [2]}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Check the Residuals\n",
    "\n",
    "So far we've assumed that the effect of experience is the same for each level of education and professional role.\n",
    "Perhaps this assumption isn't merited. We can formally test this using some interactions.\n",
    "\n",
    "We can start by seeing if our model assumptions are met. Let's look at a residuals plot, with the groups separated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "resid = lm.resid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "xticks = []\n",
    "ax = fig.add_subplot(111, xlabel='Group (E, M)', ylabel='Residuals')\n",
    "for values, group in factor_groups:\n",
    "    i,j = values\n",
    "    xticks.append(str((i, j)))\n",
    "    group_num = i*2 + j - 1 # for plotting purposes\n",
    "    x = [group_num] * len(group)\n",
    "    ax.scatter(x, resid[group.index], marker=symbols[j], color=colors[i-1],\n",
    "            s=144, edgecolors='black')\n",
    "ax.set_xticks([1,2,3,4,5,6])\n",
    "ax.set_xticklabels(xticks)\n",
    "ax.axis('tight');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Obviously, the linear model alone does not capture all model features, as the residuals are not normally distributed within each group. To improve the model, we check if interactions between the model parameters can explain the group differences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Add an Interaction\n",
    "\n",
    "#### Interaction Salary*Experience\n",
    "\n",
    "Add an interaction between salary and experience, allowing different intercepts for level of experience.\n",
    "\n",
    "$$S_i = \\beta_0+\\beta_1X_i+\\beta_2E_{i2}+\\beta_3E_{i3}+\\beta_4M_i+\\beta_5E_{i2}X_i+\\beta_6E_{i3}X_i+\\epsilon_i$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      S   R-squared:                       0.961\n",
      "Model:                            OLS   Adj. R-squared:                  0.955\n",
      "Method:                 Least Squares   F-statistic:                     158.6\n",
      "Date:                Tue, 14 Apr 2020   Prob (F-statistic):           8.23e-26\n",
      "Time:                        22:11:02   Log-Likelihood:                -379.47\n",
      "No. Observations:                  46   AIC:                             772.9\n",
      "Df Residuals:                      39   BIC:                             785.7\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "Intercept    7256.2800    549.494     13.205      0.000    6144.824    8367.736\n",
      "C(E)[T.2]    4172.5045    674.966      6.182      0.000    2807.256    5537.753\n",
      "C(E)[T.3]    3946.3649    686.693      5.747      0.000    2557.396    5335.333\n",
      "C(M)[T.1]    7102.4539    333.442     21.300      0.000    6428.005    7776.903\n",
      "X             632.2878     53.185     11.888      0.000     524.710     739.865\n",
      "C(E)[T.2]:X  -125.5147     69.863     -1.797      0.080    -266.826      15.796\n",
      "C(E)[T.3]:X  -141.2741     89.281     -1.582      0.122    -321.861      39.313\n",
      "==============================================================================\n",
      "Omnibus:                        0.432   Durbin-Watson:                   2.179\n",
      "Prob(Omnibus):                  0.806   Jarque-Bera (JB):                0.590\n",
      "Skew:                           0.144   Prob(JB):                        0.744\n",
      "Kurtosis:                       2.526   Cond. No.                         69.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "interX_lm = ols('S ~ C(E)*X + C(M)', salary_table).fit()\n",
    "print(interX_lm.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The \"Factor\" Experience has the \"Treatments\" (or elements) \"1\", \"2\", and \"3\". Since the \"Treatment 1\"([T.1]) is taken as the reference, it is not listed here explicitly. This is called \"corner-point\" approach.\n",
    "Similarly, the \"Factor\" Management has the \"Treatments\" \"0\" and \"1\". With \"0\" taken as the reference, only the term C(M)[T.1] is listed in the model.\n",
    "The interactions are described by the terms \"C(E)[T.i]:X\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "##### Test the Interaction Management*Experience\n",
    "\n",
    "Test that $\\beta_5 = \\beta_6 = 0$. We can use anova_lm or we can use an F-test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid           ssr  df_diff       ss_diff         F    Pr(>F)\n",
      "0      41.0  4.328072e+07      0.0           NaN       NaN       NaN\n",
      "1      39.0  3.941068e+07      2.0  3.870040e+06  1.914856  0.160964\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in greater\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in less\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1912: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= _a)\n"
     ]
    }
   ],
   "source": [
    "print(anova_lm(lm, interX_lm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<F test: F=array([[1.91485593]]), p=0.16096422424743717, df_denom=39, df_num=2>\n"
     ]
    }
   ],
   "source": [
    "print(interX_lm.f_test('C(E)[T.2]:X = C(E)[T.3]:X = 0'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<F test: F=array([[1.91485593]]), p=0.16096422424743717, df_denom=39, df_num=2>\n"
     ]
    }
   ],
   "source": [
    "print(interX_lm.f_test([[0,0,0,0,0,1,-1],[0,0,0,0,0,0,1]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Both tests show that the models are not significantly different. In other words, there is no interaction effect between Management and Experience in the data.\n",
    "\n",
    "The contrasts are created here under the hood by patsy.\n",
    "\n",
    "Recall that F-tests are of the form $R\\beta = q$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  0.  0.  0.  1. -1.]\n",
      " [ 0.  0.  0.  0.  0.  0.  1.]]\n",
      "[[0.]\n",
      " [0.]]\n"
     ]
    }
   ],
   "source": [
    "LC = interX_lm.model.data.orig_exog.design_info.linear_constraint('C(E)[T.2]:X = C(E)[T.3]:X = 0')\n",
    "print(LC.coefs)\n",
    "print(LC.constants)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Interact education with management"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      S   R-squared:                       0.999\n",
      "Model:                            OLS   Adj. R-squared:                  0.999\n",
      "Method:                 Least Squares   F-statistic:                     5517.\n",
      "Date:                Tue, 14 Apr 2020   Prob (F-statistic):           1.67e-55\n",
      "Time:                        22:11:02   Log-Likelihood:                -298.74\n",
      "No. Observations:                  46   AIC:                             611.5\n",
      "Df Residuals:                      39   BIC:                             624.3\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=======================================================================================\n",
      "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------\n",
      "Intercept            9472.6854     80.344    117.902      0.000    9310.175    9635.196\n",
      "C(E)[T.2]            1381.6706     77.319     17.870      0.000    1225.279    1538.063\n",
      "C(E)[T.3]            1730.7483    105.334     16.431      0.000    1517.690    1943.806\n",
      "C(M)[T.1]            3981.3769    101.175     39.351      0.000    3776.732    4186.022\n",
      "C(E)[T.2]:C(M)[T.1]  4902.5231    131.359     37.322      0.000    4636.825    5168.222\n",
      "C(E)[T.3]:C(M)[T.1]  3066.0351    149.330     20.532      0.000    2763.986    3368.084\n",
      "X                     496.9870      5.566     89.283      0.000     485.728     508.246\n",
      "==============================================================================\n",
      "Omnibus:                       74.761   Durbin-Watson:                   2.244\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1037.873\n",
      "Skew:                          -4.103   Prob(JB):                    4.25e-226\n",
      "Kurtosis:                      24.776   Cond. No.                         79.0\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "interM_lm = ols('S ~ X + C(E)*C(M)', salary_table).fit()\n",
    "print(interM_lm.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid           ssr  df_diff       ss_diff           F        Pr(>F)\n",
      "0      41.0  4.328072e+07      0.0           NaN         NaN           NaN\n",
      "1      39.0  1.178168e+06      2.0  4.210255e+07  696.844466  3.025504e-31\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in greater\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in less\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1912: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= _a)\n"
     ]
    }
   ],
   "source": [
    "print(anova_lm(lm, interM_lm))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The interaction effect Management*Education is highly significant!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "##### Check the residuals of the extended model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "infl = interM_lm.get_influence()\n",
    "resid = infl.resid_studentized_internal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111, xlabel='X', ylabel='standardized resids')\n",
    "\n",
    "for values, group in factor_groups:\n",
    "    i,j = values\n",
    "    idx = group.index\n",
    "    ax.scatter(X[idx], resid[idx], marker=symbols[j], color=colors[i-1],\n",
    "            s=144, edgecolors='black')\n",
    "ax.axis('tight');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "There looks to be an outlier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Find and Remove the Outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    student_resid       unadj_p     fdr_bh(p)\n",
      "32     -14.950832  1.676948e-17  7.713960e-16\n",
      "33       1.288001  2.055339e-01  9.599254e-01\n",
      "23       1.023194  3.126860e-01  9.599254e-01\n",
      "41       0.942329  3.519767e-01  9.599254e-01\n",
      "11       0.896574  3.755915e-01  9.599254e-01\n",
      "5        0.890643  3.787254e-01  9.599254e-01\n",
      "39       0.835081  4.088920e-01  9.599254e-01\n",
      "38       0.819164  4.178011e-01  9.599254e-01\n",
      "21       0.729762  4.700104e-01  9.599254e-01\n",
      "20      -0.726132  4.722071e-01  9.599254e-01\n",
      "30       0.704477  4.854309e-01  9.599254e-01\n",
      "28       0.603122  5.500109e-01  9.599254e-01\n",
      "44      -0.599720  5.522526e-01  9.599254e-01\n",
      "1       -0.590487  5.583601e-01  9.599254e-01\n",
      "17      -0.564832  5.755078e-01  9.599254e-01\n",
      "0       -0.482474  6.322368e-01  9.599254e-01\n",
      "34      -0.474793  6.376517e-01  9.599254e-01\n",
      "6       -0.464031  6.452728e-01  9.599254e-01\n",
      "7        0.428795  6.704934e-01  9.599254e-01\n",
      "45      -0.426443  6.721915e-01  9.599254e-01\n",
      "4        0.424446  6.736338e-01  9.599254e-01\n",
      "3       -0.418531  6.779148e-01  9.599254e-01\n",
      "35       0.382327  7.043486e-01  9.599254e-01\n",
      "43       0.360696  7.203240e-01  9.599254e-01\n",
      "12       0.357944  7.223662e-01  9.599254e-01\n",
      "37       0.342548  7.338259e-01  9.599254e-01\n",
      "2       -0.291688  7.721113e-01  9.599254e-01\n",
      "24       0.287114  7.755852e-01  9.599254e-01\n",
      "31      -0.285085  7.771273e-01  9.599254e-01\n",
      "36      -0.275068  7.847542e-01  9.599254e-01\n",
      "13      -0.268726  7.895942e-01  9.599254e-01\n",
      "27      -0.259790  7.964281e-01  9.599254e-01\n",
      "15       0.252085  8.023339e-01  9.599254e-01\n",
      "16       0.249972  8.039551e-01  9.599254e-01\n",
      "42       0.195876  8.457513e-01  9.599254e-01\n",
      "9       -0.194723  8.466475e-01  9.599254e-01\n",
      "10       0.190826  8.496777e-01  9.599254e-01\n",
      "26      -0.165977  8.690547e-01  9.599254e-01\n",
      "19       0.165168  8.696870e-01  9.599254e-01\n",
      "25      -0.161711  8.723902e-01  9.599254e-01\n",
      "14       0.147997  8.831273e-01  9.599254e-01\n",
      "29       0.147288  8.836831e-01  9.599254e-01\n",
      "40      -0.129912  8.973215e-01  9.599254e-01\n",
      "18      -0.072460  9.426159e-01  9.854621e-01\n",
      "22       0.016188  9.871691e-01  9.878792e-01\n",
      "8       -0.015292  9.878792e-01  9.878792e-01\n"
     ]
    }
   ],
   "source": [
    "outl = interM_lm.outlier_test('fdr_bh')\n",
    "outl.sort_values(by='unadj_p', inplace=True)\n",
    "print(outl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "to_drop = 32\n",
    "idx = salary_table.index.drop(to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
      "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34,\n",
      "            35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45],\n",
      "           dtype='int64')\n"
     ]
    }
   ],
   "source": [
    "print(idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "##### Rerun the original linear model without the outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      S   R-squared:                       0.955\n",
      "Model:                            OLS   Adj. R-squared:                  0.950\n",
      "Method:                 Least Squares   F-statistic:                     211.7\n",
      "Date:                Tue, 14 Apr 2020   Prob (F-statistic):           2.45e-26\n",
      "Time:                        22:11:02   Log-Likelihood:                -373.79\n",
      "No. Observations:                  45   AIC:                             757.6\n",
      "Df Residuals:                      40   BIC:                             766.6\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   8044.7518    392.781     20.482      0.000    7250.911    8838.592\n",
      "C(E)[T.2]   3129.5286    370.470      8.447      0.000    2380.780    3878.277\n",
      "C(E)[T.3]   2999.4451    416.712      7.198      0.000    2157.238    3841.652\n",
      "C(M)[T.1]   6866.9856    323.991     21.195      0.000    6212.175    7521.796\n",
      "X            545.7855     30.912     17.656      0.000     483.311     608.260\n",
      "==============================================================================\n",
      "Omnibus:                        2.511   Durbin-Watson:                   2.265\n",
      "Prob(Omnibus):                  0.285   Jarque-Bera (JB):                1.400\n",
      "Skew:                          -0.044   Prob(JB):                        0.496\n",
      "Kurtosis:                       2.140   Cond. No.                         33.1\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "lm32 = ols('S ~ C(E) + X + C(M)', data=salary_table, subset=idx).fit()\n",
    "print(lm32.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Interaction Education*Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      S   R-squared:                       0.959\n",
      "Model:                            OLS   Adj. R-squared:                  0.952\n",
      "Method:                 Least Squares   F-statistic:                     147.7\n",
      "Date:                Tue, 14 Apr 2020   Prob (F-statistic):           8.97e-25\n",
      "Time:                        22:11:02   Log-Likelihood:                -371.70\n",
      "No. Observations:                  45   AIC:                             757.4\n",
      "Df Residuals:                      38   BIC:                             770.0\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "Intercept    7266.0887    558.872     13.001      0.000    6134.711    8397.466\n",
      "C(E)[T.2]    4162.0846    685.728      6.070      0.000    2773.900    5550.269\n",
      "C(E)[T.3]    3940.4359    696.067      5.661      0.000    2531.322    5349.549\n",
      "C(M)[T.1]    7088.6387    345.587     20.512      0.000    6389.035    7788.243\n",
      "X             631.6892     53.950     11.709      0.000     522.473     740.905\n",
      "C(E)[T.2]:X  -125.5009     70.744     -1.774      0.084    -268.714      17.712\n",
      "C(E)[T.3]:X  -139.8410     90.728     -1.541      0.132    -323.511      43.829\n",
      "==============================================================================\n",
      "Omnibus:                        0.617   Durbin-Watson:                   2.194\n",
      "Prob(Omnibus):                  0.734   Jarque-Bera (JB):                0.728\n",
      "Skew:                           0.162   Prob(JB):                        0.695\n",
      "Kurtosis:                       2.468   Cond. No.                         68.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "interX_lm32 = ols('S ~ C(E) * X + C(M)', data=salary_table, subset=idx).fit()\n",
    "print(interX_lm32.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid           ssr  df_diff       ss_diff         F    Pr(>F)\n",
      "0      40.0  4.320910e+07      0.0           NaN       NaN       NaN\n",
      "1      38.0  3.937424e+07      2.0  3.834859e+06  1.850508  0.171042\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in greater\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in less\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1912: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= _a)\n"
     ]
    }
   ],
   "source": [
    "table3 = anova_lm(lm32, interX_lm32)\n",
    "print(table3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Again, this is not significant, and can be left away."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Final Result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Result from the Interaction Test: the significant model with Interaction Experience*Management, without the outlier #32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid           ssr  df_diff       ss_diff            F        Pr(>F)\n",
      "0      40.0  4.320910e+07      0.0           NaN          NaN           NaN\n",
      "1      38.0  1.711881e+05      2.0  4.303791e+07  4776.734853  2.291239e-46\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in greater\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in less\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1912: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= _a)\n"
     ]
    }
   ],
   "source": [
    "interM_lm32 = ols('S ~ X + C(E) * C(M)', data=salary_table, subset=idx).fit()\n",
    "print(anova_lm(lm32, interM_lm32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Re-plotting the residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.48729115, -0.5954801 , -0.29517124, -0.4230286 ,  0.42897925,\n",
       "        0.89301298, -0.46877102,  0.43335347, -0.01549185, -0.19716976,\n",
       "        0.1932281 ,  0.89883686,  0.36201307, -0.27198058,  0.14988891,\n",
       "        0.25516709,  0.25303223, -0.56982837, -0.07340216,  0.16726751,\n",
       "       -0.73057304,  0.73417535,  0.01639955,  1.022579  ,  0.29055183,\n",
       "       -0.16376864, -0.16808615, -0.26295279,  0.60810224,  0.14917126,\n",
       "        0.70907133, -0.28850311, -5.77349886,  1.27725604, -0.47957968,\n",
       "        0.38658255, -0.2783867 ,  0.34649164,  0.82264053,  0.83833986,\n",
       "       -0.13158075,  0.94368563,  0.19833606,  0.36478747, -0.60470452,\n",
       "       -0.43098731])"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "resid = interM_lm32.get_influence().summary_frame()['standard_resid']\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111, xlabel='X[~[32]]', ylabel='standardized resids')\n",
    "\n",
    "for values, group in factor_groups:\n",
    "    ii,jj = values\n",
    "    idx = group.index\n",
    "    if to_drop in idx:\n",
    "        idx = idx.drop(to_drop)\n",
    "    if len(idx)>0:\n",
    "        ax.scatter(X[idx], resid[idx], marker=symbols[jj], color=colors[ii-1], s=144, edgecolors='black')\n",
    "ax.axis('tight');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "A final plot of the fitted values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lm_final = ols('S ~ X + C(E)*C(M)', data=salary_table.drop([32])).fit()\n",
    "mf = lm_final.model.data.orig_exog\n",
    "lstyle = ['-','--']\n",
    "\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111, xlabel='Experience', ylabel='Salary')\n",
    "\n",
    "for values, group in factor_groups:\n",
    "    i,j = values\n",
    "    idx = group.index\n",
    "    if to_drop in idx:\n",
    "        idx = idx.drop(to_drop)\n",
    "    ax.scatter(X[idx], S[idx], marker=symbols[j], color=colors[i-1],\n",
    "            s=144, edgecolors='black')\n",
    "    # drop NA because there is no idx 32 in the final model\n",
    "    ax.plot(mf.X[idx].dropna(), lm_final.fittedvalues[idx].dropna(),\n",
    "            ls=lstyle[j], color=colors[i-1])\n",
    "ax.axis('tight');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "##### Interaction Plot Salary | Experience\n",
    "\n",
    "From our first look at the data, the difference between Master's and PhD in the management group is different than in the non-management group. This is an interaction between the two qualitative variables management, M and education, E. We can visualize this by first removing the effect of experience, then plotting the means within each of the 6 groups using interaction.plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "U = S - X * interX_lm32.params['X']\n",
    "U.name = 'Salary|X'\n",
    "\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111)\n",
    "ax = interaction_plot(E, M, U, colors=['red','blue'], markers=['^','D'],\n",
    "        markersize=10, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Example 2: Minority Employment Data - ABLine plotting"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "TEST  - Job Aptitude Test Score\n",
    "ETHN  - 1 if minority, 0 otherwise\n",
    "JPERF - Job performance evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "from urllib.request import urlopen\n",
    "url_base = 'https://raw.githubusercontent.com/thomas-haslwanter/statsintro_python/master/ipynb/Data/data_others/'\n",
    "inFile = 'minority.table'\n",
    "url = url_base + inFile\n",
    "minority_table = pd.read_table(urlopen(url))\n",
    "\n",
    "#minority_table = pd.read_csv(r'.\\Data\\data_others\\minority.table')\n",
    "minority_table.to_csv('minority.table', sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x22af9c41708>"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "factor_group = minority_table.groupby(['ETHN'])\n",
    "\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111, xlabel='TEST', ylabel='JPERF')\n",
    "colors = ['purple', 'green']\n",
    "markers = ['o', 'v']\n",
    "for factor, group in factor_group:\n",
    "    ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
    "                marker=markers[factor], s=12**2)\n",
    "ax.legend(['ETHN == 1', 'ETHN == 0'], scatterpoints=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  JPERF   R-squared:                       0.517\n",
      "Model:                            OLS   Adj. R-squared:                  0.490\n",
      "Method:                 Least Squares   F-statistic:                     19.25\n",
      "Date:                Tue, 14 Apr 2020   Prob (F-statistic):           0.000356\n",
      "Time:                        22:11:03   Log-Likelihood:                -36.614\n",
      "No. Observations:                  20   AIC:                             77.23\n",
      "Df Residuals:                      18   BIC:                             79.22\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      1.0350      0.868      1.192      0.249      -0.789       2.859\n",
      "TEST           2.3605      0.538      4.387      0.000       1.230       3.491\n",
      "==============================================================================\n",
      "Omnibus:                        0.324   Durbin-Watson:                   2.896\n",
      "Prob(Omnibus):                  0.850   Jarque-Bera (JB):                0.483\n",
      "Skew:                          -0.186   Prob(JB):                        0.785\n",
      "Kurtosis:                       2.336   Cond. No.                         5.26\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "min_lm = ols('JPERF ~ TEST', data=minority_table).fit()\n",
    "print(min_lm.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111, xlabel='TEST', ylabel='JPERF')\n",
    "for factor, group in factor_group:\n",
    "    ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
    "                marker=markers[factor], s=12**2)\n",
    "ax.legend(['ETHN == 1', 'ETHN == 0'], scatterpoints=1, loc='upper left')\n",
    "fig = abline_plot(model_results = min_lm, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  JPERF   R-squared:                       0.632\n",
      "Model:                            OLS   Adj. R-squared:                  0.589\n",
      "Method:                 Least Squares   F-statistic:                     14.59\n",
      "Date:                Tue, 14 Apr 2020   Prob (F-statistic):           0.000204\n",
      "Time:                        22:11:03   Log-Likelihood:                -33.891\n",
      "No. Observations:                  20   AIC:                             73.78\n",
      "Df Residuals:                      17   BIC:                             76.77\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      1.1211      0.780      1.437      0.169      -0.525       2.768\n",
      "TEST           1.8276      0.536      3.412      0.003       0.698       2.958\n",
      "TEST:ETHN      0.9161      0.397      2.306      0.034       0.078       1.754\n",
      "==============================================================================\n",
      "Omnibus:                        0.388   Durbin-Watson:                   3.008\n",
      "Prob(Omnibus):                  0.823   Jarque-Bera (JB):                0.514\n",
      "Skew:                           0.050   Prob(JB):                        0.773\n",
      "Kurtosis:                       2.221   Cond. No.                         5.96\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "min_lm2 = ols('JPERF ~ TEST + TEST:ETHN', data=minority_table).fit()\n",
    "print(min_lm2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xiIhICTvd9FhTY7kQ2w5uo++nfek5uycA8ffEM+/OeVxx8RU2J6t4VI5FRERK2N+nx5oay/nKzs9m7I9jaTG1BUt3LuWVG15hw2Mb6NGoh93RKiyVYxEREQ84dXqsqbGcK2MMn2/8nKbvNGX88vEMbDGQpMeTePKaJ3Xss4epHIuIiHjAn9NjL8tLU2M5Jxv2b+D6mddz55d3UrNqTX4e9DMf3/IxdYPq2h2tUlA5FhER8ZCYzjGEVQ/T1FiK5VDOIaK/iybivQg27N/Au73e5beHf+Oay66xO1ql4mN3ABERkYqqTlAdtg3ZZncMKeMKCguY7pzOMz8+w8GcgzzW9jFe6PoCFwdcbHe0SknlWERERMQmq/asIjo+mrV719L58s7E9oilde3Wdseq1FSORURERErZvj/28dQPTzFz3UxCg0L59NZPuaPFHTrEowxQORYREREpJfkF+cT+GssLP71AXkEeT3d6mmeufYaL/C6yO5qcoHIsIiIiUgq+3/Y9Q+KHkJSZRO8mvXnjpjdodHEju2PJ36gci4iIiHjQ9kPbGb5oOPOS5tHo4kYsuHsBPRv3tDuWnIHKsYiIiIgHHHMdY+LPE3l1xav4ePkwsdtEhl09jCo+VeyOJkVQORYREREpQcYYvtj8BSO+H8Geo3u4u9XdvHrDq4RWC7U7mhSDyrGIiIhICdl0YBPR8dEs3bmUiNoRzL51Np0u62R3LDkHKsciIiIiF+hw7mGeW/Ycb69+m2D/YKb2nMojbR7B28vb7mhyjlSORURERM5ToSlkhnMGTy95msycTB5t8ygvdn2RkKohdkeT86RyLCIiInIefk39lcfjH2dN+ho6XdaJ2B6xRNaJtDuWXCCVYxEREZFzsP+P/YxeMpoPEz6kblBdZg2YxV0t79LpdhWEyrGIiIhIMbgKXLy1+i2e/+l5clw5PHXNU4y5dgxBVYLsjiYlSOVYRERE5CwWb1vM0IVDSfw9kZ6NezLlpik0DmlsdyzxAJVjERERkTPYeXgnwxcN5+stX3NFjSuYf9d8ejfpbXcs8SCVYxEREZG/yXHl8MqKV3hlxSt4WV6Mv348wzsMx9/H3+5o4mEqxyIiIiInGGP4KvErRnw/gl1HdnFnyzt5rftr1KtWz+5oUkpUjkVERESAzRmbGRI/hCU7ltCqViuWPbCM68KuszuWlDKVYxEREanUjuQe4bllz/HW6reoVqUab9/8No+2fRQfL9WkykjfdREREamUCk0hHyV8xOglo8nIzuCRNo/w0vUvcUnVS+yOJjbyWDm2LCsc+PyUf9UQeNYYM8VT1xQREREpjtVpq4mOj2Z12mo61u9I/D3xRNWJsjuWlAEeK8fGmCQgAsCyLG8gDfjaU9cTEREROZsD2Qd4+oenmZ4wnToX1eHjWz7mnlb36HQ7Oam0llV0A7YZY3aV0vVERERETnIVuJj621TGLRvHMdcxRnUcRUznGJ1uJ/9QWuX4TuDT091gWdYjwCMAl112WSnFERERkcrixx0/MiR+CJsyNnHTFTfxZo83Cb8k3O5YUkZ5efoClmX5AX2BOae73RjzvjGmrTGmbc2aNT0dR0RERCqJXYd3MXDOQLrN7EaOO4d5d84j/p54FWMpUmlMjm8GHMaY/aVwLREREankclw5vLbyNSb+PBGAl7q+xIiOI3S6nRRLaZTjuzjDkgoRERGRkmKMYe6WuQz/fjg7D+/k9ha3M6n7JOoH17c7mpQjHi3HlmVVBboDj3ryOiIiIlK5JWYkMnThUBZvX0zLWi1Z+sBSuoR1sTuWlEMeLcfGmGNAiCevISIiIpXX0byjPL/seWJXx3KR30XE9ojlsXaP6XQ7OW965oiIiEi5U2gK+Xjdxzz1w1McyD7AQ5EPMaHbBGoG6s39cmFUjkVERKRcWZO+huj4aH5J/YWr613Nt3d/S9u6be2OJRWEyrGIiIiUCxnZGTyz5Bk+cH5ArcBafNjvQ+5rfR9elsd3ppVKROVYREREyjR3oZupv03l2aXPku3KZniH4Tx73bNUq1LN7mhSAakci4iISJm1dMdShiwcwsYDG+nesDtv9niTZjWb2R1LKjCVYxERESlzdh/ZzcjvRzJn8xzCqofx9R1f0y+8H5Zl2R1NKjiVYxERESkzct25TFo5iQnLJ2AwPN/leUZ1HEWAb4Dd0aSSUDkWERER2xlj+CbpG55Y9AQ7Du/gtua3Man7JC6vfrnd0aSSUTkWERERWyX9nsTQhUNZtG0RzWs254f7fqBbw252x5JKSuVYREREbHE07ygv/vQiU36dQqBvIFNumsK/2/0bX29fu6NJJaZyLCIiIqWq0BQya/0snvzhSfb/sZ8HIx9kQrcJ1AqsZXc0EZVjERERKT2OvQ4e/+5xVqWuon1oe7658xvahbazO5bISSrHIiIi4nG/H/udMUvGMM0xjZqBNZnRbwb3t75fp9tJmaNyLCIiIh7jLnTz3pr3iFkaw9G8owy7ehjjrhtHsH+w3dFETkvlWERERDzip50/MWThENbvX0+3Bt2IvTmW5jWbn/PjGGNIW53GqkmrSPkuBVeOC98AXxr3akzHkR2p266uDgeREqNyLCIiIiUq9WgqoxaP4rONn3F58OV8efuX3NL0lvMqsAWuAubeP5ekb5Jw57oxhQYA1zEXiV8mkvJdCuF9wuk/sz/evt4l/VuRSkgLfURERKRE5LpzmbB8AuFvhzN3y1zGXTeOzf/ZzIBmA86rGBtjThZj1zHXyWJ88vZCgyvbxZZ5W5h7/1yMMWd4JJHi0+RYRERELogxhm+Tv+WJRU+w7dA2BjQbwOs3vk5Y9bALety01WkkzT9ejIviznGTND+J9N/SCW0fekHXFNHkWERERM5bcmYyvWb3ou9nffHz9mPxfYv58vYvL7gYA6x6fRXuHHex7uvOcbPq9VUXfE0RTY5FRETknGXlZfHS/17ijV/eIMA3gMk3Tubx9o+X6Ol2KQtS/rGU4kxMoSF5QXKJXVsqL5VjERERKTZjDLM3zObJH54kPSudf0X8i4ndJnLpRZeW+LVcOUUvp/i74k6ZRYqiciwiIiLF4tzrJDo+mhV7VtCubju+uv0rrqp3lceu5xvge9b1xqfyCVCtkQunNcciIiJSpMxjmTz27WO0ndaW5Mxk4vrE8cvgXzxajAEa92qM5VW8XS4sL4smvZp4NI9UDvoRS0RERE6roLCA99a+x9gfx3I07yjR7aN5rstzVPevXirX7zCiw/FDP7LPPj328fehw4gOpZBKKjqVYxEREfmH5buWEx0fzbr96+ga1pXYm2NpWatlqWYIbR9KeJ9wtszbUuR6Yp8AH8L7hlO3Xd1STCcVlZZViIiIyElpR9O4+8u76fxhZw7lHmLOwDksuX9JqRdjAMuy6D+zP037NcU30PcfSywsLwvfqr407deU/jP76whpKRGaHIuIiAh57jze+OUNXvrfS7gL3Tzb+Vme6vQUVX2r2prL29ebAbMHkP5bOisnrSTluxTcOW58Anxo0qsJHUZ2ILSdDv6QkqNyLCIiUsl9l/IdwxYOI+VgCv2b9mfyjZNpUKOB3bFOsiyL0PahDPzvQLujSCWgciwiIlJJbT24lWELh7EgZQHhIeEsuncRN15xo92xRGylciwiIlLJ/JH/BxOWT+D1Va9TxbsKk7pPIvqqaPy8/eyOJmI7lWMREZFKwhjDZxs/Y9TiUaRlpfFA6wd4udvL1AmqY3c0kTJD5VhERKQSWLdvHdHx0SzfvZw2ddowZ+AcOtTXvsAif6dyLCIiUoEdzDlIzI8xvLv2XS4OuJhpfaYxKGIQ3l7edkcTKZNUjkVERCqggsICpjmmMebHMRzOPcx/2v2H57s8T42AGnZHEynTVI5FREQqmBW7VxAdH41zn5PrLr+Ot25+i1aXtrI7lki5oHIsIiJSQaRnpfPUD0/xyfpPqFetHp/f9jkDmw/UyXEi50DlWEREpJzLL8hnyi9TePF/L5JfkM+Ya8fwdKenCfQLtDuaSLmjciwiIlKOxafEM2zRMJIzk+kb3pfJN07miouvsDuWSLmlciwiIlIObTu4jScWPcH85Pk0CWlC/D3x9GjUw+5YIuWeyrGIiEg5kp2fzYTlE5i0ahJ+3n68esOrDL16qE63EykhKsciIiLlgDGG/276LyMXjyT1aCr3Xnkvr9zwCnWD6todTaRCUTkWEREp4zbs38CQhUNYtnMZkbUj+ezWz7jmsmvsjiVSIakci4iIlFGHcg7x7NJnmbpmKjX8a/Bur3cZHDVYp9uJeJDKsYiISBlTUFjAdOd0nvnxGQ7mHOSxto/xQtcXuDjgYrujiVR4KsciIiJlyKo9q4iOj2bt3rV0vrwzsT1iaV27td2xRCoNlWMREZEyYG/WXkYvGc3MdTMJDQrl01s/5Y4Wd+h0O5FSpnIsIiJio/yCfGJ/jeWFn14gryCPZzo9w9PXPs1FfhfZHU2kUlI5FhERscn3275nSPwQkjKT6N2kN2/c9AaNLm5kdyyRSk3lWEREpJRtP7Sd4YuGMy9pHo0ubsSCuxfQs3FPu2OJCB4ux5ZlVQfigJaAAR40xqzy5DVFRETKqmOuY0z8eSKvrngVHy8fJnabyLCrh1HFp4rd0UTkBE9Pjt8EFhpjbrMsyw+o6uHriYiIlDnGGL7Y/AUjvh/BnqN7uLvV3bx6w6uEVgu1O5qI/I3HyrFlWdWAzsC/AIwx+UC+p64nIiJSFm08sJEh8UNYunMpEbUjmH3rbDpd1snuWCJyBp6cHDcEMoAZlmW1BtYCQ40x2R68poiISJlwOPcw45aO453f3iHYP5ipPafySJtHdLqdSBnn5cHH9gGigP/PGBMJZAOj/34ny7IesSxrjWVZazIyMjwYR0RExPMKTSFxjjgav9WYt397m0faPELy48k81u4xFWORcsCT5TgVSDXG/Hri8y84Xpb/whjzvjGmrTGmbc2aNT0YR0RExLN+Tf2Vq+Ku4uH5D9P0kqasfWQtU3tNJaRqiN3RRKSYPLaswhizz7KsPZZlhRtjkoBuwGZPXU9ERMQu+//Yz+glo/kw4UPqBtVl1oBZ3NXyLp1uJ1IOeXq3imhg1omdKrYDgzx8PRGRC2aMIW11GqsmrSLluxRcOS58A3xp3KsxHUd2pG67uio9AoCrwMVbq9/i+Z+eJ8eVw1PXPMWYa8cQVCXI7mgicp48Wo6NMQlAW09eQ0SkJBW4Cph7/1ySvknCnevGFBoAXMdcJH6ZSMp3KYT3Caf/zP54+2r9aGW2eNtihi4cSuLvifRs3JMpN02hcUhju2OJSBHys/PZ/EXRCxl0Qp6IyAnGmJPF2HXM9c/bCw2ubBdb5m1h7v1zGTB7gCbIldDOwzsZvmg4X2/5mitqXMH8u+bTu0lvu2OJSBH2OvaydtpaNs7eSN7RvCLvq3IsInJC2uo0kuafvhifyp3jJml+Eum/pRPaXoc4VBbHXMd45edXeHXlq3hZXky4fgLDOwzX6XYiZVTu4Vw2zN6AI87BPuc+fPx9aHF7CyIHR/Jc5+fO+HUqxyIiJ6x6fRXuHHex7uvOcbPq9VXc9vltHk4ldjPG8FXiVwz/fji7j+zmzpZ38lr316hXrZ7d0UTkb4wx7P55N844J5vmbMKd46Z2RG16vtOTVne3wr+6/1kfQ+VYROSElAUpJ9cYn40pNCQvSPZwIrHb5ozNDIkfwpIdS2hVqxXLHljGdWHX2R1LRP4m+0A262auwxHnIDMpE78gP1o/0JqowVHUbVP3nB5L5VhE5ARXTtHLKf6uuFNmKX+O5B7huWXP8dbqt6hWpRpv3/w2j7Z9FB8v/bUpUlaYQsO2xdtwxjnZMm8Lha5C6l9Tn06jO9F8YHP8Av3O63H1p1xE5ATfAN+zrjc+lU+A/hda0RSaQj5K+IjRS0aTkZ3BI20e4aXrX+KSqpfYHU1ETjiaehTndCfO6U6O7DpCQEgA7aPbEzU4iprNLvxAOf2fXUTkhMa9GpP4ZWKxllZYXhZNejUphVRSWlanrSY6PprVaavpWL8j8ffEE1XnHwe7iogNClwFJH+bjDPOydaFWzGFhobdG9L91e6E9wvHp0rJVVqVYxGRE2M4sBAAACAASURBVDqM6HD80I/ss0+Pffx96DCiQymkEk/b/8d+nlnyDNMTplPnojp8fMvH3NPqHm3TJ1IGZKZk4vzAScKHCWTvzyaobhCdnulE5IOR1GhQwyPXVDkWETkhtH0o4X3C2TJvS5HriX0CfAjvG07dduf2Jg8pW1wFLt757R3GLRtHjiuHUR1HEdM5RqfbidjMnetm85ebccY52blsJ5a3RZPeTYgaHEWjHo3w8vHy6PVVjkVETrAsi/4z+x8/CGR+Eu4c91+WWFheFj7+x4tx/5n9NVksx37c8SPR8dFszthMj0Y9mHLTFMIvCbc7lkiltn/9fhxxDtZ/sp7cQ7nUaFiD6ydcT8S/IgiqU3o/tKoci4icwtvXmwGzB5D+WzorJ60k5bsU3DlufAJ8aNKrCR1GdiC0nQ7+KK92Hd7FyMUj+WLzFzSs0ZB5d86jT5M++kFHxCZ5WXls/GwjzjgnaavT8PbzptmtzYgaHEVYlzAsr9L/s2kZU7w9PUtD27ZtzZo1a+yOISIiFUyOK4fXVr7GxJ8nAjDm2jGM6DgCf5+zHwggIiXLGEPar2k44hxs/GwjrmwXNVvUJOrhKK6890qqhlT1eAbLstYaY9qe7jZNjkVEpMIyxjB3y1yGfz+cnYd3cnuL25nUfRL1g+vbHU2k0sk5mMO6j9fhjHNyYOMBfAN9aXlnS6IGRxF6VWiZeQVH5VhERCqkxIxEhi4cyuLti2lZqyVLH1hKl7AudscSqVRMoWHnsp044hwkfpVIQV4Boe1D6f1+b1re2ZIqQVXsjvgPKsciIlKhHM07yvPLnid2dSwX+V1EbI9YHmv3mE63EylFWXuzSPgwAecHTg5tO4R/dX/aPNKGqMFRXHrlpXbHK5L+TyEiIhVCoSnk43Uf89QPT3Eg+wCDowYz/vrx1Ay88BOzROTsCt2FbF24FUecg+RvkzEFhrAuYXR5vgvNBjTDN8DX7ojFonIsIiLl3pr0NUTHR/NL6i9cXe9qvr37W9rWPe17bUSkhB3acQjndCcJMxLISssi8NJAOo7sSORDkYQ0DrE73jlTORYRkXIrIzuDZ5Y8wwfOD6gVWIsP+33Ifa3vw8vy7CEBIpWdO89N0rwkHHEOtv+wHcuyaNSjETe/dTNNejfB29fb7ojnTeVYRETKHXehm6m/TeXZpc+S7cpmeIfhPHvds1SrUs3uaCIVWkZixvGDOmau59jvxwi+LJguz3UhYlAEwfWD7Y5XIlSORUSkXFm6YylDFg5h44GNdG/YnTd7vEmzms3sjiVSYeVn57N5zmYccQ72rNiDl68XTfs1JXJwJA1vaIiXd8V6pUblWEREyoXdR3Yz8vuRzNk8h7DqYXx9x9f0C+9XZvZGFalo0temHz+oY/ZG8o7mERIeQvfXutP6/tYE1gq0O57HqByLiEiZluvOZdLKSUxYPgGAF7q8wMiOIwnwDbA5mUjFk3s4lw2zN+CIc7DPuQ+fAB9aDGxB1MNR1L+mfqX4YVTlWEREyiRjDN8kfcMTi55gx+EdDGw+kNe6v8bl1S+3O5pIhWKMYffPu3HGOdk0ZxPuHDe1I2rT852etLq7Ff7VK9cx6yrHIiJS5iT9nsTQhUNZtG0RLWq2YMn9S7i+wfV2xxKpULIPZLNu5joccQ4ykzLxC/Kj9QOtafNwG+pE1bE7nm1UjkVEpMw4mneUF396kSm/TiHQN5ApN03h3+3+ja93+Tg8QKSsM4WGbYu34YxzsmXeFgpdhdS/pj6dRnei+cDm+AX62R3RdirHIiJiu0JTyKz1s3jyhyfZ/8d+Hox8kAndJlArsJbd0UQqhCN7jpAwIwHndCdHdh0hICSA9tHtiRocRc1mOkXyVCrHIiJiK8deB49/9zirUlfRPrQ939z5De1C29kdS6TcK3AVkPxtMs44J1sXbsUUGhp2b0j3V7sT3i8cnyqqgaej/yoiImKL34/9zpglY5jmmEbNwJrM6DeD+1vfr9PtRC5QZkomzg+cJHyYQPb+bILqBtHpmU5EPhhJjQY17I5X5qkci4hIqXIXunl3zbvELI3hj/w/GHb1MMZdN45g/4pxupaIHVw5LhK/SsQxzcGun3ZheVs06d2EqMFRNOrRCC8f/dBZXCrHIiJSan7a+RPR8dFsOLCBGxrewJs93qR5zeZ2xxIpt/av33/8OOdP1pN7KJcaDWtw/YTrifhXBEF1guyOVy6pHIuIiMelHk1l1OJRfLbxMy4Pvpwvb/+SW5reUikOFBDPMcaQtjqNVZNWkfJdCq4cF74BvjTu1ZiOIztSt13dCvkcy8vKY+NnG3HGOUlbnYa3nzfNbm1G1OAowrqEYXlVvN9zaVI5FhERj8l15zJ51WTGLx9PoSlk3HXjePKaJ6nqW9XuaFLOFbgKmHv/XJK+ScKd68YUGgBcx1wkfplIyncphPcJp//M/nj7etuc9sIZY0j7Ne34cc6fbcSV7aJmi5rcNOUmrrz3SqqG6M9USVE5FhGREmeM4dvkb3li0RNsO7SNAc0G8PqNrxNWPczuaFIBGGNOFmPXMdc/by80uLJdbJm3hbn3z2XA7AHldoJ8LPMY6z9ZjzPOyYGNB/AN9KXlnS2JejiK0Pah5fb3VZapHIuISIlKzkxm2MJhxG+Np9klzVh832JuaHiD3bGkAklbnUbS/NMX41O5c9wkzU8i/bd0QtuHllK6C2cKDTuX7cQxzUHiV4kU5BcQ2j6UPtP60OKOFlQJqmJ3xApN5VhEREpEVl4WL/3vJd745Q0CfAOYfONkHm//uE63kxK36vVVuHPcxbqvO8fNqtdXcdvnt3k41YXL2ptFwocJOD9wcmjbIfyr+9Pm0TZEDY7i0isvtTtepaFyLCIiF8QYw+wNsxm1eBR7/9jLoIhBvNztZS69SH+Zi2ekLEg5ucb4bEyhIXlBsocTnb9CdyFbF27FEecg+dtkTIEhrEsYXZ7vQrMBzfAN0A+XpU3lWEREzptzr5Po+GhW7FlBu7rt+PqOr7mq3lV2x5IKzpVT9HKKvyvulLk0HdpxCOd0JwkzEshKyyLw0kA6juxI5EORhDQOsTtepaZyLCIi5yzzWCZjfxzL+473CQkIIa5PHIMiB+l0OykVvgG+Z11vfCqfgLJRd9x5bpLmJeGY5mD7D9uxvCwa9WhEz7d70rhX4wqxq0ZFUDaeLSIiUi4UFBbw3tr3GPvjWI7mHSW6fTTPdXmO6v7V7Y4mlUjjXo1J/DKxWEsrLC+LJr2alEKqM8vYnIHjAwfrZ67n2O/HCL4smC7PdyFiUATB9XUyZFmjciwiIsWyfNdyouOjWbd/HV3DuhJ7cywta7W0O5ZUQh1GdDh+6Ef22afHPv4+dBjRoRRS/VV+dj6b52zGMc3BnpV78PL1omm/pkQ9HEWDbg3w8tarLGWVyrGIiBQp7WgaoxaP4tONn3JZ8GXMGTiHW5vdqv1VxTah7UMJ7xPOlnlbilxP7BPgQ3jfcOq2q1tq2dLXpuOY5mDD7A3kZ+UTEh5C99e60/r+1gTWCiy1HHL+VI5FROS08tx5vPHLG7z0v5dwF7p5tvOzPNXpKZ1uJ7azLIv+M/sfPwhkfhLuHPdfllhYXhY+/seLcf+Z/T3+g1zu4Vw2zN6AI87BPuc+fAJ8aDGwBVEPR1H/mvr6QbKcUTkWEZF/WJC8gGGLhrH14Fb6N+3P5Bsn06BGA7tjiZzk7evNgNkDSP8tnZWTVpLyXQruHDc+AT406dWEDiM7ENrOcwd/GGPY/fNunHFONs3ZhDvHTe2I2vR8pyet7m6Ff3V/j11bPEvlWERETtp6cCvDFg5jQcoCwkPCWXTvIm684ka7Y4mclmVZhLYPZeB/B5baNbMPZLNu5joccQ4ykzKpUq0KrR9oTZuH21Anqk6p5RDPUTkWERH+yP+DCcsn8Pqq16niXYVJ3ScRfVU0ft5+dkcTsV1hQSHbf9iOY5qDpHlJFLoLqX9NfTo93YnmtzXHL1B/TioSlWMRkUrMGMNnGz9j1OJRpGWl8UDrB3i528vUCdIETOTIniPHD+qYnsCR3UcICAmg/ZD2RA2OomazmnbHEw9RORYRqaTW7VtHdHw0y3cvp02dNswZOIcO9Ut/yyuRsqTAVUDyt8k4pjnYunArGGjYvSHdX+tOeL9wfKqoOlV0+g6LiFQyB3MOEvNjDO+ufZeLAy5mWp9pDIoYhLeXTueSyiszJRPnB04SPkwge382QXWDuHbMtUQ+GEmNBjXsjielSOVYRKSSKCgsYJpjGmN+HMOR3CP8p91/eL7L89QIKB9/8debXI+0rLRi3z80KJTU4akeTCTlnSvHReJXiTimOdj10y4sb4smvZsQ9XAUjW5qhJePDuqojFSORUQqgRW7VxAdH41zn5MuYV2I7RFLq0tb2R3rnPQN78sHzg/IL8g/6339vP3oF96vFFJJebR//X7WTlvLhk82kHs4lxpX1KDby91o/UBrguoE2R1PbObRcmxZ1k4gCygA3MaYtp68noiI/FV6VjpP/fAUn6z/hHrV6vH5bZ8zsPnAcnkoQUznGGYkzCjWfb0tb2Kui/FwIilP8rLy2PjZRhzTHKT/lo63nzfNbm1G1MNRhF0XhuVV/v5MiGeUxuS4qzHm91K4joiInJBfkM+UX6bw4v9eJL8gnzHXjuHpTk8T6Fd+j6+tE1SHQRGDzjo99vP2Y1DEIGpfVLsU00lZZIwh7dc01k5by6bPN+HKdlGrZS16vNmDVve0omqITnuUf9KyChGRCiY+JZ5hi4aRnJlM3/C+TL5xMldcfIXdsUpEcabHmhrLscxjrP9kPY5pDjI2ZeAb6EvLO1sS9XAUoe1Dy+UrJ1J6PF2ODfC9ZVkGeM8Y876HryciUmltO7iNJxY9wfzk+TQJaUL8PfH0aNTD7lgl6mzTY02NKy9TaNi5bCeOaQ4Sv0qkIL+A0Pah9JnWhxZ3tKBKUBW7I0o5YRljPPfgllXXGJNuWVYtYDEQbYz539/u8wjwCMBll13WZteuXR7LIyJSEWXnZzNh+QQmrZqEn7cfz3Z+lqFXD62wp9vtzdpLw9iG5Lpz/3FbgE8A24duVzmuRLL2ZpHwYQLOD5wc2nYI/+r+XHnflUQNjuLSKy+1O56UUZZlrT3Te+HOODm2LOsyY8zuC7mwMSb9xD8PWJb1NdAe+N/f7vM+8D5A27ZtPdfURUQqGGMM/930X0YuHknq0VTuu/I+Jt4wkbpBde2O5lFnmh5ralx5FLoL2bpwK45pDpIXJGMKDGFdwuj6Qlea3tIU3wBfuyNKOVbUsoq5QBSAZVlfGmNuPZcHtiwrEPAyxmSd+PhG4IXzTioiIiet37+eIfFD+GnXT0TWjuSzWz/jmsuusTtWqTnd2mOtNa74Du04dPygjhkJZKVnEXhpIB1HdSTywUhCGofYHU8qiKLK8amr1Ruex2NfCnx9YtG7DzDbGLPwPB5HREROOJRziGeXPsvUNVOp4V+Dd3u9y+CowZXudLu/T481Na643HlutszdgjPOyfYftmN5WTTq0Yie7/Skca/GePtWrue+eF5R5dic4eNiMcZsB1qfcyIREfmHgsICpjun88yPz3Aw5yCPtX2MF7q+wMUBF9sdzTanTo81Na54MjZn4IhzsG7mOnIycwi+LJguz3chYlAEwfWD7Y4nFVhR5bi1ZVlHOT5BDjjxMSc+N8aYah5PJyIirNqziuj4aNbuXUvnyzsT2yOW1rU1e/hzevze2vc0Na4g8rPz2TxnM45pDvas3IOXrxdN+zUl6uEoGnRrgJe3jnMWzztjOTbG6HUKEREb7c3ay+glo5m5biahQaF8euun3NHiDu3ReoqYzjEs2rZIU+NyzBjDXsdeHNMcbJi9gfysfELCQ+g+qTut72tNYK3ye3CNlE/nvM+xZVnVgf8YY8Z7II+ISKWXX5BP7K+xvPDTC+QV5PFMp2d4+tqnucjvIrujlTl1guqwbcg2u2PIecg9nMv6WetxxjnZl7APnwAfWtzegqjBUdS/pr5+CBTbFLWVW30gBqjL8Z0rZgMvAvef+FhERErYoq2LGLpwKEmZSfRu0ps3bnqDRhc3sjuWSIkwxrD75904pjnYPGcz7lw3tSNr03NqT1rd1Qr/6v52RxQpcnI8E/gJ+BLoAfwCbAJaGWP2lUI2EZFKY/uh7QxfNJx5SfNofHFjFty9gJ6Ne9odS6REZB/IJuGjBJxxTjKTM6lSrQqt/9WaNg+3oU5UHbvjifxFUeX4YmPMcyc+XmRZ1n6gnTEmz/OxREQqh2OuY0z8eSKvrngVHy8fJnabyLCrh1HFR0fdSvlWWFDI9h+245jmIGleEoXuQupfU59Oz3Si+W3N8QusmCc4SvlX5Jpjy7Jq8P/2O94HVD1xoAfGmIMeziYiUmEZY/hi8xeM+H4Ee47u4e5Wd/PqDa8SWi3U7mgiF+TIniM4pztJmJ7Akd1HqHpJVa4aehWRD0VSs1lNu+OJnFVR5TgYWMtfDwNxnPin4fwOBhERqfQ2HtjIkPghLN25lIjaEcy+dTadLutkdyw5D8YY0lansWrSKlK+S8GV48I3wJfGvRrTcWRH6rarWyneWFbgKiD522Qc0xxsXbgVDDTs3pDuk7oT3jccnyrn/P5/EdsUtZVbWCnmEBGp8A7nHmbc0nG889s7BPsHM7XnVB5p80ilO92uoihwFTD3/rkkfZOEO9eNKTx+XpbrmIvELxNJ+S6F8D7h9J/Zv8Ke4paZknn8oI4P15F9IJug0CA6j+1MxKAIajSoYXc8kfNS1G4V9xpjPjnx8TXGmBWn3Pa4Mebt0ggoIlLeFZpCpjun8/SSpzmYc5BH2zzKi11fJKRqiN3R5DwZY04WY9cx1z9vLzS4sl1smbeFuffPZcDsARVmguzKOV7+HXEOdv20C8vboknvJkQ9HEWjmxrh5aODOqR8K+p1juHAJyc+fguIOuW2BwGVYxGRs/gl9Rei46NZk76GTpd14q2b3yKidoTdseQCpa1OI2n+6Yvxqdw5bpLmJ5H+Wzqh7cv3evJ96/bhiHOw4ZMN5B7OpcYVNej2cjdaP9CaoDpBdscTKTFFlWPrDB+f7nMRETnFvj/2MfqH0Xy07iPqBtVl1oBZ3NXyrgozPazsVr2+CneOu1j3dee4WfX6Km77/DYPpyp5eVl5bPx0I444B+m/pePt502zW5sR9XAUYdeFYXnp+SwVT1Hl2Jzh49N9LiIigKvAxVur3+L5n54nx5XDU9c8xZhrxxBURZO1iiRlQcrJNcZnYwoNyQuSPZyo5BhjSPs1jbXT1rLp8024sl3UalmLHm/2oNU9ragaUtXuiCIeVVQ5bmpZ1nqOT4mvOPExJz7XThUiZVy9yfVIy0or9v1Dg0JJHZ7qwUQV3+Jtixm6cCiJvyfSs3FPptw0hcYhje2OJR7gyil6OcXfFXfKbKdjmcdY//F6HHEOMjZl4BvoS8u7WhI1OIrQ9qF61UMqjaLKcbNSSyEiJa5veF8+cH5AfkH+We/r5+1Hv/B+pZCqYtp5eCfDFw3n6y1fc0WNK5h/13x6N+ltdyzxIN8A37OuNz6VT0DZ3MrMFBp2LN2BM85J4leJFOQXEHpVKH2m9aHFHS2oEqTDaKTyKWort12WZfUHGgEbjDGLSi+WiFyomM4xzEiYUaz7elvexFwX4+FEFc8x1zFe+fkVXl35Kl6WFxOun8DwDsN1ul0l0LhXYxK/TCzW0grLy6JJryalkKr4stKzSPgwAecHTg5tP4R/DX/a/F8boh6K4tIrL7U7noititrKbSrQAlgJvGhZVntjzIullkxELkidoDoMihh01umxn7cfgyIGUfui2qWYrnwzxvBV4lcM/344u4/s5q6Wd/Fq91epV62e3dGklHQY0eH4oR/ZZ58e+/j70GFEh1JIVbRCdyEp8Sk445wkL0jGFBjCuoTR9cWuNL2lKb4BvnZHFCkTinqdpzPQ2hhTYFlWVWA5oHIsUo4UZ3qsqfG52ZyxmSHxQ1iyYwlXXnolH9/yMZ0v72x3LClloe1DCe8TzpZ5W4pcT+wT4EN433Dqtqtbiun+6tCOQzg/cJIwI4Gs9CwCLw2k46iORD4YSUhj7bUt8ndFleN8Y0wBgDHmmKWV+CLlztmmx5oaF9+R3CM8t+w53lr9FtWqVOPtm9/m0baP4uNVNteSimdZlkX/mf2PHwQyPwl3jvsvSywsLwsf/+PFuP/M/qX+ZjZ3npstc7fgjHOy/YftWF4WjW5uRM93etK4V+MKe2KfSEmwjDn9einLsnKAFP7fnsZXAFtPfG6MMVeWdJi2bduaNWvWlPTDilRqe7P20jC2Ibnu3H/cFuATwPah21WOi1BoCvko4SNGLxlNRnYGj7R5hJeuf4lLql5idzQpA4wxpP+WzspJK0n5LgV3jhufAB+a9GpCh5EdCG1Xugd/ZGzOOH6c88x15GTmEHx5MJEPRRLxrwiC6weXahaRssyyrLXGmLanu63IrdzQfsYi5d6ZpseaGp/d6rTVRMdHszptNR3rdyT+nnii6kSd/Qul0rAsi9D2oQz870DbMuRn57Ppv5twxjnZs3IPXr5eNO3XlKiHo2h4Q0Md1CFyjooqxxs5cznOsyxrGzDGGLOk5GOJSEk63dpjrTU+s/1/7OeZJc8wPWE6dS6qw8e3fMw9re7RPq9SZhhj2Lt27/HjnGdvID8rn5DwELpP6k7r+1oTWCvQ7ogi5VZRW7md8Tgny7K8gZbArBP/FJEy7O/TY02NT89V4OKd395h3LJx5LhyGNVxFDGdY3S6nZQZuYdzWT9rPc44J/sS9uET4EOL21sQNTiK+tfU1w9wIiXgvN5JcuKNeussy3qrhPOIiIecOj3W1PiflmxfwpCFQ9icsZkejXow5aYphF8SbncsEYwx7F6+G0ecg81zNuPOdVM7sjY9p/ak1V2t8K/ub3dEkQrlgt5mbYx5r6SCiIhn/Tk9fm/te5oan2LX4V2M+H4EXyZ+ScMaDZl35zz6NOmjCZzYLvtANgkfJeCMc5KZnEmValWIGBRB1OAo6kTVsTueSIWlPYhEKpGYzjEs2rZIU2Mgx5XDaytfY+LPEwF4qetLjOg4An8fTeHEPoUFhWxfvB1HnIOkeUkUugu5rNNldHqmE81va45foJ/dEUUqPJVjkUqkTlAdtg3ZZncMWxljmLtlLsO/H87Owzu5vcXtTOo+ifrB9e2OJpXYkd1HcM5wkjA9gSO7j1D1kqpcNfQqIh+KpGazmnbHE6lUVI5FpNJIzEhk6MKhLN6+mJa1WrL0gaV0CetidyyppApcBSTPT8YR52Drwq1goGH3hnSf1J3wvuH4VNFf0SJ20J88EanwjuYd5fllzxO7OpaL/C4itkcsj7V7TKfbiS0ykzNxfOBg3YfryD6QTVBoEJ3HdiZiUAQ1GtSwO55Ipae/GUSkwio0hcxcN5PRP4zmQPYBBkcNZvz146kZqJeppXS5clwkfpmII87Brp92YXlbhPcJJ3JwJI1uaoSXj5fdEUXkBJVjEamQ1qSvITo+ml9Sf+Hqelfz7d3f0rbuaU8KFfGYfev2HT+o45MN5B7OpcYVNej2cjdaP9CaoDraP1ukLFI5FpEKJSM7g6eXPM1053RqBdbio/4fce+V9+JlaTInpSMvK4+Nn27EEecg/bd0vKt40/zW5kQOjiTsujAd5yxSxqkci0iF4C50M/W3qTy79FmyXdkM7zCcZ697lmpVqtkdTSoBYwypv6TiiHOw6fNNuLJd1GpZix5v9uDKe68k4OIAuyOKSDGpHItIubd0x1KGLBzCxgMb6d6wO2/2eJNmNZvZHUsqgWOZx1j/8XoccQ4yNmXgG+hLy7taEjU4itD2oTpMRqQcUjkWkXJr95HdjPx+JHM2zyGsehhf3/E1/cL7qZCIR5lCw46lO3DGOUn8KpGC/AJCrwqlz7Q+tLijBVWCqtgdUUQugMqxiJQ7ue5cJq2cxITlEwB4ocsLjOw4kgBfvXQtnpOVnkXChwk4P3ByaPsh/Gv40+b/2hD1UBSXXnmp3fFEpISoHItIuWGM4Zukb3hi0RPsOLyDgc0H8lr317i8+uV2R5MKqtBdSEp8Cs44J8kLkjEFhrCuYXR9sSvNBjTDx19/jYpUNPpTLSLlwpbftzBs4TAWbVtEi5otWHL/Eq5vcL3dsaSCOrT9EM7pThJmJJCVnkXgpYF0HNWRyAcjCWkcYnc8EfEglWMRKdOO5h3lxZ9eZMqvUwj0DWTKTVP4d7t/4+vta3c0qWDceW62zN2CM87J9h+2Y3lZNLq5ET3f6UnjXo3x9vW2O6KIlAKVYxEpkwpNIbPWz+LJH55k/x/7eTDyQSZ0m0CtwFp2R5MKJmNzBo44B+tmriMnM4fgy4Pp8kIXIv4VQXD9YLvjiUgpUzkWkTLHsdfB4989zqrUVbQPbc83d35Du9B2dseSCiQ/O59N/92EM87JnpV78PL1omn/pkQNjqLhDQ11UIdIJaZyLCJlxu/HfmfMkjFMc0yjZmBNZvSbwf2t79fpdlIijDHsXbv3+HHOszeQn5XPJU0vofuk7rS+rzWBtQLtjigiZYDKsYjYzl3o5t017xKzNIY/8v9g2NXDGHfdOIL99ZK2XLjcw7msn7UeZ5yTfQn78AnwocXtLYgaHEX9a+prX2wR+QuVYxGx1U87fyI6PpoNBzZwQ8MbeLPHmzSv2dzuWFLOGWPYvXw3jjgHm+dsxp3rpnZkbXpO7Umru1vhH+xvd0QRKaNUjkXEFnuO7GHU4lF8vulz/v/27jw+qvre//jrZA+EtWEJYYcQdjMjiyCNCLIUBBGlXrVqEfRef7csAq6Val2oVxEVr21tAiguuKEoBUSKVmkNVTiTACGECAiERLYAIWSbyXx/fyT1UosQkOTMJO/n48GjJHOS11LXXQAAIABJREFUeSOnhzdfvud8OjTpwPKfL+fa7tdqFU9+lKKDRWQszcCT6uHozqNENo4kaXIS7qlu4txxTscTkSCgciwitarUV8ozXzzDvL/Nw2/8PHLFI9x7+b2abicXzF/hZ/e63dipNtkfZOP3+Wk/pD1DHhxCz+t7EtEwwumIIhJEVI5FpFYYY/jzzj9z99q72XVsFxN7TOSZkc/QsWlHp6NJkDqx7wSeJR7SF6dzYt8JGsQ2YOCMgbimuGjRo4XT8UQkSKkci0iN23l0JzM/msmar9fQI7YH625Zx1Wdr3I6lgShCm8FO1fuxE6x+Xrt1wB0GdGFEfNH0P2a7oRGaFCHiPw4KsciUmNOlp3k8c8f59mNzxIdHs2CkQv41YBfabqdnLejO49iL7LJeDmDU4dO0Si+EckPJeO63UXTjk2djicidUiNl2PLskKBTcABY8zVNf1+IuI8YwxvbH2De9bdQ35RPpOTJvO74b+jVUwrp6NJEPGWeMlanoWdarP3s71YoRaJ4xJxTXXRdVRXQsL0/GsRufhqY+V4BpAFNK6F9xIRh3nyPUxbM42/7/87/dv05/0b3mdg24FOx5Ig8m3Gt5WDOl7bSunxUpp1acbw3w3nktsuoVFcI6fjiUgdV6Pl2LKstsBY4AlgVk2+l4g462jxUR765CFe2vwSsQ1iWTR+Eb9M+qWm20m1lBWWse3NbdgpNnmb8giNDKXndT1xTXXR8YqOGucsIrWmpleOnwPuBfRXfZE6qsJfwUubX+KhTx6isKyQ6QOn88jQR2gapX2gcnbGGHI35mKn2mS+mYm32EvL3i0Z/fxo+v6iL9HN9Xg/Eal9NVaOLcu6GjhkjNlsWdbQsxx3J3AnQPv27WsqjojUgA17NzBtzTQyDmZwZccrWfizhfRu2dvpWBLgio8Ws+XVLdipNoczDxPeMJzeN/XGPdVN/IB4DYIREUfV5Mrx5cB4y7LGAFFAY8uyXjPG/OL0g4wxfwL+BNCvXz9Tg3lE5CI5UHiAe9bdw7Jty2jfpD3vTHqH63pcp1IjP8j4DXs+3YMn1UPWe1lUlFcQPzCecSnj6HVDLyIbRTodUUQEqMFybIx5AHgAoGrleM73i7GIBJcyXxnPbnyWxz9/HJ/fx2+Sf8N9Q+6jQXgDp6NJgDqZd5L0l9PxLPJwbPcxoppFcel/XYp7iptWffX0EhEJPHrOcQAxxnDgywOkzU8jZ3UO3hIv4dHhJIxNYPCcwbTp30Yrc+KYVTtXMXPtTL4u+JoJ3SewYOQCOjXr5HQsCUB+n5+cNTl4Uj3sXLUTU2HoeGVHrnzsSnpM7EFYlP7oEZHAVStXKGPMX4G/1sZ7BasKbwUrbl1B9ofZ+Ep9GH/lDhNvceVzPnNW55A4LpEJSycQGq4JUFJ7co7mcPfau1mVs4rusd1Z+4u1jOwy0ulYEoCO7T6GZ7GH9CXpnMw7ScNWDRl8z2Bct7v4ScJPnI4nIlIt+ut7ADDGfFeMvcXef3/db/Ce8rLjgx2suHUFE9+YqBVkqXFF5UU88fkTLNi4gMjQSOaPmM+0gdOICI1wOpoEEF+Zjx0rdmCn2OxZvwcrxKLrz7oy5sUxJIxN0F/mRSToqBwHgANfHiB75ZmL8el8JT6yV2aT91Ue8QPiaymd1DfGGN7c9ib3rLuHAycPcNslt/HkVU/SOqa109EkgBzefhg71SZjaQYlR0to0qEJQx8dimuyi8ZtNfNJRIKXynEASHsmDV+Jr1rH+kp8pD2TxvVvXV/DqaQ+yvg2g2lrprFh3wYujbuUdya9w6B2g5yOJQGi/FQ5mW9n4kn1sP+L/YSEh9B9QnfcU910vqqzBnWISJ2gchwAclblfLfH+FyM37Bz1c4aTiT1TUFJAXM/mcsfN/+R5tHNSRmXwuSkyYSG6J/E6ztjDPmb8yvHOb+xlfKT5cR2j2XE/BFccsslNGzZ0OmIIiIXlcpxAPCWnH07xfdVd5VZ5Fwq/BWk2Cn8+pNfc6L0BP/d/7/57dDf0iy6mdPRxGElx0rY+sZW7BSbgxkHCYsOo9fPe+Ge6qbd5e1034OI1FkqxwEgPDr8nPuNTxcWrd82+fH+tu9vTFszjfRv0xnacSgLRy+kT6s+TscSBxlj2LdhH3aKzfZ3t+Mr9RHnjmPM78fQ56Y+RDWJcjqiiEiNU8sKAAljE8hanlWtrRVWiEW3sd1qIZXUVXkn87h33b28vvV12jZuy1vXv8WknpO0EliPFR0sIuOVDDyLPBzdeZTIxpEkTU7CPdVNnDvO6XgiIrVK5TgADJo9qHLox6lzrx6HRYUxaLZukJLzV15RznMbn+Oxzx/DW+HloZ8+xP1D7qdhhPaM1kf+Cj+71+3GTrHJ/jAbv89P+yHtGfLgEHpN6kV4g3CnI4qIOELlOADED4gncVwiOz7Ycdb9xGHRYSSOT6RN/za1mE7qgjU5a5i5diY7j+5kfOJ4nh31LJ2bdXY6ljjgxL4TeJZ4SF+czol9J2gQ24CBMwbimuKiRY8WTscTEXGcynEAsCyLCUsnVA4CWZmNr8T3L1ssrBCLsKjKYjxh6QT987dU266CXdy99m5W7lxJt590Y83NaxjddbTTsaSWVXgr2LlyJ3aKzddrvwagy4gujJg/gu7XdCc0Qk8lERH5J5XjABEaHsrENyaS91UeX8z/gpzVOfhKfIRFh9FtbDcGzRlEfH8N/pDqOVV+inkb5jE/bT4RoRE8ddVTzLhshqbb1TNHdx6tHNTxSganDp2iUXwjkh9KxnW7i6YdmzodT0QkIKkcBxDLsogfEM+ktyc5HUWClDGGtzPfZs66OeQW5nJL31t48qonadNIW3HqC2+Jl6zlWdgpNns/34sVapE4LhHXVBddR3clJDTE6YgiIgFN5VikjthycAvT10zns72f4Wrt4s3r3uTy9pc7HUtqybcZ32Kn2Gx5bQtlJ8po1qUZw383nKRfJhHTOsbpeCIiQUPlWCTIFZQU8PCnD/P7Tb+nWVQzXrr6Jaa4pmi6XT1QVljGtje3YafY5G3KIzQylJ7X9cQ11UXHKzpqnLOIyAVQORYJUhX+ChZ5FvHg+gc5VnqMu/rdxaNXPkrz6OZOR5MaZIwhd2MudqpN5puZeIu9tOzdktHPj6bvL/oS3Tza6YgiIkFN5VgkCKXtT2Pammlszt9McodkFo5eyCWtL3E6ltSg4iPFbHltC3aqzeHMw4Q3DKf3Tb1xT3UTPyBeT7EREblIVI5Fgkj+yXzuX38/SzOWEt8onmXXLeOGXjeoGNVRxm/Y8+ke7BSbHe/voKK8gviB8YxLGUevG3oR2SjS6YgiInWOyrFIECivKGfhPxby6GePUlZRxoNDHuSBnz5ATIRutKqLTuadxLPEg2eRh+N7jhPVLIpL/+tS3FPdtOrTyul4IiJ1msqxSIBb+/VaZnw0g+yj2Vzd7WqeHfUsXZt3dTqWXGR+n5+c1TnYqTY5q3IwfkPHKzsy7Ilh9Li2B2FRulyLiNQGXW1FAtTuY7uZtXYWH2R/QELzBFbdtIoxCWOcjiUX2bHdx7AX2aQvSacov4iGrRoy+N7BuKe4ad5VN1eKiNQ2lWORAFPsLeZ3G37H0188TVhIGE8Of5KZl80kMkz7S+sKX5mPHSt2YKfY7Fm/ByvEouvPuuKe6iZhbAKh4XoMn4iIU1SORQKEMYZ3t7/L7I9ns79wPzf1uYmnrnqK+MYaG15XHMo8hJ1qs+XVLZQcLaFJhyYMfXQorskuGrdt7HQ8ERFB5VgkIGw7tI3pa6bz6TefktQ6iTeue4Mh7Yc4HUsugvJT5WS+lYmdapOblktIeAjdJ3THfYebzsM7a1CHiEiAUTkWcdDx0uM8/OnDvPjVizSJasLvx/yeOy+9U9PtgpwxhvzN+WxO2cy2ZdsoP1lObPdYRswfwSW3XkLDFg2djigiIj9A5VjEAX7jZ7FnMQ+sf4CCkgL+89L/5LErH+MnDX7idDT5EUqOlbD19a3YqTYHMw4SFh1Gr5/3wn2Hm3aD2+l51CIiQUDlWKSWbczdyLQ109iUt4kh7Yfwws9eIKl1ktOx5AIZY9i3YR92is32d7fjK/UR545jzO/H0OemPkQ1iXI6ooiInAeVY5Fa8m3Rt9z/l/t5JeMV2jRqw+sTX+fG3jdqNTFIFR0sIuOVDDyLPBzdeZTIxpEkTU7CPdVNnDvO6XgiInKBVI5Fapi3wssLX77AI399hFJfKfdffj+/Tv61ptsFIX+Fn93rdmOn2GR/mI3f56f9kPYMeXAIvSb1IrxBuNMRRUTkR1I5FqlB63atY8ZHM8g6ksWYhDE8N+o5En6S4HQsOU8n9p3As9iDZ7GHwv2FNIhtwMAZA3FPdRPbPdbpeCIichGpHIvUgG+Of8OstbN4f8f7dGnWhZU3ruTqblc7HUvOQ0V5Bdkrs/Gkevh67dcAdBnRhVELRpE4PpHQCD1RRESkLlI5FrmIir3F/M/f/oenvniKECuEecPmMWvQLE23u8jaLmjLgZMHqn18fKN4cmflVuvYozuPYqfaZLySwalDp2gU34jkh5Jx3e6iacemFxpZRESChMqxyEVgjOG9rPeY9fEs9p3Yx429b+SpEU/RtnFbp6PVSeMTx7PIs4jyivJzHhsRGsE1idec9RhviZes5VnYKTZ7P9+LFWqROC4R11QXXUd3JSQ05GJFFxGRAKdyLPIjZR7KZMZHM1i/Zz19W/Xl1WtfJblDstOx6rS5yXNZkr6kWseGWqHMvWLuGV/7NuNb7BSbLa9toexEGc26NGP474aT9MskYlrrhkkRkfpI5VjkAh0vPc5v//pbXvjyBRpHNubFMS9y56V3Ehai/1vVtLhGcUxOmnzO1eOI0AgmJ02mdUzr7z5XVljG1mVb8aR6yNuUR2hkKD2v64n7DjcdkjtonLOIBC1jDAe+PEDa/DRyVufgLfESHh1OwtgEBs8ZTJv+bfT40GqwjDFOZ/hOv379zKZNm5yOIXJWfuPn5fSXeWD9Axw+dZg7L72Tx4c9TmwDPbWgNuWfzKfzws6U+kp/8JjosGh2z9hNq4atyE3LxU61yXwrE2+xl5Z9WuK+w03fm/sS3Ty6FpOLiFx8Fd4KVty6guwPs/GV+jD+/+t3VohFWHQYieMSmbB0AqHhuqHYsqzNxph+Z3pNS1wi5+HLA18ybc00vjzwJYPbDWbNzWtwx7mdjlUvnWv1OCI0gts73c6elD28l/oeh7cfJrxhOL1v6s2ld1yqFRQRqTOMMd8VY2+x999f9xu8p7zs+GAHK25dwcQ3Jur6dxYqxyLVcLDoIA+uf5DF6YuJi4nj1Wtf5eY+N+vi4rAz7T22/Bad9nSif3p/WmW34uPyj4kfGM+41HH0+nkvIhvpySEiUrcc+PIA2SvPXIxP5yvxkb0ym7yv8ogfEF9L6YKPyrHIWXgrvLz41Ys8/NeHKfGWcM/ge5ibPJdGkY2cjib86+px5LFIktKTcNtumh1vhj/GT7//6od7qptWfVo5HVVEpMakPZOGr8RXrWN9JT7Snknj+reur+FUwUvlWOQHrN+9nukfTWf74e2M7jqa50Y9R2JsotOx5DR+n59bim6h8PVCumR3IcSEsKfjHjaM2MAHv/+AtrF6lJ6I1H05q3L+ZY/x2Ri/YeeqnTWcKLipHIt8z97je5n98WyWZy2nc7POfPAfHzCu2zhtoQggx3Yfw15kk74knaL8Ijo368zGIRvZlLSJopZFTHVNVTEWkXrDW3L27RTfV91V5vpK5VikSom3hKf+/hRP/v1JQqwQHr/ycWYPnk1UWJTT0QTwlfnY8f4O7FSbPev3YIVYdP1ZV9x3uGn000Y8/YenKfWVEm1F/+BzjUVE6qLw6PBz7jc+XVi06t/Z6L+O1HvGGFbsWMGsj2fxzfFvuKHXDTw94mnaNWnndDQBDmUewk612bJ0CyUFJTTp0IShjw7FNdlF47aNvztuctJkXtr80r8911hEpK5LGJtA1vKsam2tsEIsuo3tVgupgpfKsdRrWYezmPHRDNbtXkfvlr359LZPGdpxqNOx6r3yU+VkvpWJnWqTm5ZLSHgI3Sd0x32Hm87DO59xUMfc5Lms3bVWq8YiUu8Mmj2ocujHqXOvHodFhTFo9qBaSBW8VI6lXiosK+S3f/0tC79cSExEDAtHL+Su/ndpup2DjDHkb85nc8pmti3bRvnJcmK7xzJi/gguufUSGrZoeNavj2sUx67pu2oprYhI4IgfEE/iuER2fLDjrPuJw6LDSByfSJv+bWoxXfBRE5B6xW/8LM1Yyv1/uZ9Dpw4x1T2VJ4Y9QYuGLZyOVm+VHCth6+tbsVNtDmYcJCw6jF4/74X7DjftBrfTjZAiIudgWRYTlk6oHASyMhtfyRkm5EVVFuMJSyfounoOKsdSb2zK28S0NdPYmLuRy9pexp9v+jP92pxxcqTUMGMMez/fiyfVw/Z3t+Mr9RHnjmPsH8bS+8beRDXRTZAiIucjNDyUiW9MJO+rPL6Y/wU5q3PwlfgIiw6j29huDJoziPj+GvxRHSrHUucdOnWocrqdZzEtG7bklQmv8Iu+vyDECnE6Wr1TdLCIjFcysFNtCnIKiGwcSdLtSbinuIlzxzkdT0QkqFmWRfyAeCa9PcnpKEFN5VjqLJ/fx++/+j2/+fQ3nPKeYvag2cy9Yi6NIxuf+4vlovFX+Nn18S48qR6yP8zG7/PTfkh7kh9Kpuf1PQlvEO50RBERke+oHEud9OmeT5m2ZhqZhzMZ2WUkz49+nu6x3Z2OVa+c2HcCz2IPnsUeCvcX0iC2AQNnDMQ91U1s91in48l5MMZw4MsDpM1Pq7wjvsRLeHQ4CWMTGDxnMG36t9EeRhGpM1SOpU7Zd2Ifcz6ewzvb36Fj0468f8P7XJN4jf7griUV5RVkr8zGk+rh67VfA9BlRBdGLRhF4vhEQiNCHU54dm0XtOXAyQPVPj6+UTy5s3JrMJHzKrwVlTf5fJiNr/T/bvLxFnvJWp5FzuocEsdV3uQTGh7Yv78iItVRY+XYsqwo4HMgsup93jXGPFxT7yf1W6mvlPlfzGfehnkAPDr0UeYMnkN0eLTDyeqHI9lH8CzykPFKBqcOnaJx28Ykz03GNdlF045NnY5XbeMTx7PIs4jyivJzHhsRGsE1idfUQirnGGO+K8Znmr5l/AbvKS87PtjBiltXMPGNifqLqIgEPcuYc09TuaBvXHmFbGiMKbIsKxz4GzDDGLPxh76mX79+ZtOmTTWSR+omYwwfZn/I3WvvZs/xPUzqOYmnRzxNh6YdnI5W53lLvGx/dzueVA97P9+LFWqROC4R9x1uuozqQkho8N3wmH8yn84LO1PqKz3nsdFh0eyesbtOT+PL/UcuS4cvrdZggdCoUNoNbseBjQe07UJEAp5lWZuNMWd8ZFWNrRybytZdVPVheNWPmmniUi/tOLKDmR/NZO2utfRq0Yv1t65nWKdhTseq875N/7ZynPNrWyg7UUbzrs0Z/uRwkm5LIqZ1jNPxfpS4RnFMTpp8ztXjiNCIejGmOu2ZtLMOFDhdRWkF33zyzXcfa9uFiASrGt1zbFlWKLAZ6Aq8aIz5R02+n9QPhWWFPPbZYzz3j+doGN6Q50c/z1397iI8VE89qCllhWVsXbYVT6qHvE15hEaG0vO6nrjvcNPhig51alVwbvJclqQvOesxoVZovRhTnbMq518GCZwvbbsQkWBUo+XYGFMBJFmW1RR437Ks3saYbacfY1nWncCdAO3bt6/JOBLk/MbPa1te476/3MfBooPc7rqdecPn0bJhS6ej1UnGGHLTcrFTbTLfysRb7KVln5aMXjiavjf3Jbp53dzPfa7V4/qyagyVW2cuBl+Jj+yV2eR9lUf8AA0hEJHAVmN7jv/tjSzrYeCUMWb+Dx2jPcfyQ+x8m1+t/hVpuWkMiB/A//7sf+kf39/pWHVS8ZFiMl7NwJPq4fD2w4Q3DKf3jb259I5L683e0bPtPa4Pe43/aV7DeWe8Ee9CWCEWPa/vyfVvXX9Rvp+IyI/hyJ5jy7JaAF5jzHHLsqKBq4D/qan3k7rpSPERfr3+16TYKbRo2IIl1yzh1ktu1XS7i8z4DXs+2YOdarPj/R1UlFcQPzCecanj6PXzXkQ2inQ6Yq36odXj+rRqDJAwNoGs5Vk/amvFPxm/YeeqnRchlYhIzarJbRVxwCtV+45DgLeNMX+uwfeTOsTn9/HHTX9k7qdzKSovYuZlM3n4iodpEtXE6Wh1SuGBQtJfTsezyMPxPceJahZFv7v64ZriolWfVk7Hc9SZ9h7Xl73G/zRo9qDKoR/VeFpFdVT35j4RESfV5NMqtgCumvr+Und99s1nTFszja2HtnJV56t4fvTz9GzR0+lYdYbf5ydndQ52qv3dDVcdr+zIsCeG0ePaHoRFaTYQ/PvqcX1bNQaIHxBP4rhEdnyw46IU27BonVsiEvh0pZKAsf/Efu5Zdw9vZb5FhyYdWP7z5Vzb/dp6sce1NhTsKsCz2EP6knSK8ouIaR3D4HsH457ipnnX5k7HC0inrx7Xt1VjAMuymLB0QuUgkJXZ+Ep8F7zFwgqx6Da220VOKCJy8akci+NKfaU888UzzPvbPPzGzyNXPMK9l9+r6XYXga/Mx473d2Cn2uxZvwcrxKLrz7rivsNNwpgEPXf2HP65evzS5pfq3arxP4WGhzLxjYnkfZXHF/O/IGd1Dr4SH2HRYbS9rC37/74fX+m5V5XDosIYNHtQLSQWEflxau1pFdWhp1XUL8YY/rzzz8xcO5Pdx3ZzXY/rmD9yPh2bdnQ6WtA7lHmoclDH0i2UFJTQtGNTXFNcJP0yicZtGzsdL6jkn8xnyJIh/P32v9fLcnw2xhjeu+m9c267CIsOo/s13fWcYxEJGI48rULkbHYe3cnMj2ay5us19Ijtwbpb1nFV56ucjhXUyovKyXw7EzvVJjctl5DwEHpc2wPXVBedh3fGClEpuRBxjeLYNX2X0zEC0rm2XVghFmFRYSSOr5yQp2IsIsFA5Vhq1cmykzz++eM8u/FZosOjWTByAb8a8CtNt7tAxhjyNuVhp9psW7aN8pPlxHaPZeQzI+l7S18atmjodESp48627aLb2G4MmjOI+P4a/CEiwUPlWGqFMYY3tr7BPevuIb8on8lJk/nd8N/RKqZ+Py7sQpUcK2Hr61uxU20OZhwkLDqMXj/vhfsON+0Gt9MKndQqy7KIHxDPpLcnOR1FRORHUzmWGufJ9zBtzTT+vv/v9G/Tn/dveJ+BbQc6HSvoGGPY+/lePKketr+7HV+pjzh3HGP/MJbeN/YmqkmU0xFFRESCnsqx1JijxUd56JOHeGnzS8Q2iGXR+EX8MumXmm53nooOFpHxSgZ2qk1BTgGRjSNJuj0J9xQ3ce44p+OJiIjUKSrHctH5/D7+tPlPPPTJQxSWFTJj4AweHvowTaOaOh0taPgr/Oz6eBeeVA/ZH2bj9/lpP6Q9yQ8l0/P6noQ30B5tERGRmqByLBfVhr0bmLZmGhkHMxjWaRgLRy+kV8teTscKGsf3Hid9STqexR4K9xfSoEUDBs4ciHuKm9jusU7HExERqfNUjuWiOFB4gHvW3cOybcto36Q970x6h+t6XKcbw6qhoryC7JXZeFI9fL32awC6jOzCqAWjSByfSGiEBnWIiIjUFpVj+VHKfGU8u/FZHv/8cXx+H79J/g33DbmPBuENnI4W8I5kH8GzyEPGKxmcOnSKxm0bkzw3GddkF007aguKiIiIE1SO5YKt2rmKmWtn8nXB10zoPoEFIxfQqVknp2MFNG+xl+3Lt2On2OzbsA8r1CJxXCLuO9x0GdWFkFDdrCgiIuIklWM5bzlHc7h77d2syllF99jurP3FWkZ2Gel0rID2bfq3leOcX9tC2YkymndtzvAnh5N0WxIxrWOcjiciIiJVVI6l2orKi3ji8ydYsHEBkaGRzB8xn2kDpxERGuF0tIBUVljG1mVb8aR6yNuUR2hkKD2v74l7qpsOV3TQfmwREZEApHIs52SMYdm2Zdyz7h7yTuZx2yW38eRVT9I6prXT0QKOMYbctFzsVJvMtzLxFntp2acloxeOpu/NfYluHu10RBERETkLlWM5q4xvM5i2Zhob9m3g0rhLeXfSuwxqN8jpWAGn+EgxGa9m4En1cHj7YSJiIuhzcx/cU9206d9Gq8QiIiJBQuVYzqigpIC5n8zlj5v/SPPo5qSMS+F21+2abnca4zfs+WQPdqrNjvd3UFFeQfzAeMaljqPXz3sR2SjS6YgiIiJynlSO5V9U+CtIsVP49Se/5kTpCf67/3/z26G/pVl0M6ejBYzCA4Wkv5yOZ5GH43uOE9Usin539cM1xUWrPq2cjiciIiI/gsqxfOdv+/7GtDXTSP82naEdh7Jw9EL6tOrjdKyA4Pf5yVmdg51ik7M6B+M3dBrWiWFPDKPHtT0Ii9L/lUREROoC/Yku5J3M49519/L61tdp27gtb13/FpN6TtI+WaBgVwGexR7Sl6RTlF9ETOsYLr/vcly3u2jetbnT8UREROQiUzmux8p8ZTy38Tke+/wxfH4fD/30Ie4fcj8NIxo6Hc1RvlIfO1bswE612bN+D1aIRcKYBFxTXSSMSSA0XOOcRURE6iqV43pqTc4aZnw0g5yCHK5JvIYFoxbQuVlnp2M56lDmocpBHUu3UFJQQtOOTbnysStJ+mUSjds2djqeiIiI1AKV43pmV8EuZq6dyZ93/pluP+nGmpvXMLrraKdjOaa8qJzMtzOxU2xyN+YSEh5Cj2t74JrqovPwzlgh2loiIiJSn6gc1xOnyk8xb8M85qfNJyI0gqeueooZl82ol9PtjDHkbcrDTrHZtmwb5UXlxHaPZeQzI+l7S18atqjf20pERETqM5XjOs4Yw9uZbzOBB0ZsAAAYOUlEQVRn3RxyC3O5pe8tPHnVk7Rp1MbpaLWu5FgJW1/fip1ic3DLQcKiw+h9Q29cU120G9xONyCKiIiIynFdtuXgFqavmc5nez/D1drFm9e9yeXtL3c6Vq0yxrD38714Uj1sf3c7vlIfcZfGMfYPY+l9Y2+imkQ5HVFEREQCiMpxHVRQUsDDnz7M7zf9nmZRzXjp6peY4ppCaEj9ecpC0cEiMl7JwE61KcgpILJxJEm3J+Ge6ibOFed0PBEREQlQKsd1SIW/gkWeRTy4/kGOlR7jrn538eiVj9I8un48j9df4WfXx7uwU2x2rtyJ3+en/ZD2JD+UTM/rexLeINzpiCIiIhLgVI7riLT9afxqza+w822SOyTzws9eoG+rvk7HqhXH9x4nfUk6nsUeCvcX0qBFAwbOHIh7ipvY7rFOxxMREZEgonIc5PJP5nP/+vtZmrGU+EbxLLtuGTf0uqHO31xWUV5B9sps7BSbXR/vAqDLyC6MWjCKxPGJhEbUny0kIiIicvGoHAep8opyFv5jIY9+9ihlFWU8OORBHvjpA8RExDgdrUYdyT6CnWqT8UoGxYeLady2Mclzk3FNdtG0Y1On44mIiEiQUzkOQmu/XsuMj2aQfTSbq7tdzbOjnqVr865Ox6ox3mIv25dvx06x2bdhHyFhIXQb1w33VDddRnUhJDTE6YgiIiJSR6gcB5Hdx3Yza+0sPsj+gITmCay6aRVjEsY4HavGfJv+beU459e2UHaijOZdmzP8yeEk3ZZETOu6vUIuIiIizlA5DgLF3mJ+t+F3PP3F04SFhPHk8CeZedlMIsMinY520ZUVlrF1WeWgjvzN+YRGhtLz+p64p7rpcEWHOr+XWkRERJylchzAjDG8s/0d5nw8h/2F+7m5z808NeKpOjfdzhhDbloudopN5tuZeIu9tOzTktELR9P35r5EN492OqKIiIjUEyrHAWrboW1MXzOdT7/5lKTWSbxx3RsMaT/E6VgXVfGRYjKWVg7qOJJ1hIiYCPrc3Af3VDdt+rfRKrGIiIjUOpXjAHO89DgPf/owL371Ik2imvCHsX/gDvcddWa6nfEb9nyyBzvFJuv9LPxeP20va8u41HH0vqE3ETERTkcUERGRekzlOED4jZ/FnsU8sP4BCkoK+M9L/5PHrnyMnzT4idPRLorCA4Wkv5yOZ5GH43uOE908mv7/rz+uKS5a9WnldDwRERERQOU4IGzM3ci0NdPYlLeJIe2H8MLPXiCpdZLTsX40v89Pzuoc7BSbnNU5GL+h07BODHtiGD2u7UFYlE4/ERERCSxqJw76tuhb7v/L/byS8QptGrXh9Ymvc2PvG4N+r23BrgI8izykv5xOUX4RMa1juPy+y3Hd7qJ51+ZOxxMRERH5QSrHDvBWeHnhyxd45K+PUOor5f7L7+fXyb8O6ul2vlIfO1bswE6x2fPJHqwQi4QxCbimukgYk0BoeN3YMy0iIiJ1m8pxLVu3ax3TP5rOjiM7GJMwhudGPUfCTxKcjnXBDm07VDmo49UtlBSU0LRjU6587EqSfplE47aNnY4nIiIicl5UjmvJN8e/YdbaWby/4326Nu/Kn2/8M2O7jXU61gUpLypn21vb8KR6yN2YS0h4CD2u7YFrqovOwztjhQT3thARERGpv1SOa1ixt5j/+dv/8NQXTxFihTBv2DxmDZoVdNPtjDHkbcrDTrHZtmwb5UXlxHaPZeQzI+l7S18atmjodEQRERGRH03luIYYY3gv6z1mfTyLfSf2cWPvG3lqxFO0bdzW6WjnpeRYCVtfrxznfHDLQcKiw+h9Q29cU120G9wu6G8eFBERETmdynENyDyUyYyPZrB+z3r6turLq9e+SnKHZKdjVZsxhr2f78VOsdn+7nYqyiqIuzSOsX8YS+8bexPVJMrpiCLV1nZBWw6cPFDt4+MbxZM7K7cGE4mISCBTOb6Ijpce57d//S0vfPkCjSMb8+KYF7nz0jsJCwmO/8xFB4u+G9RRkFNAZJNIXFNcuKe6iXPFOR1P5IKMTxzPIs8iyivKz3lsRGgE1yReUwupREQkUAVHawtwfuPn5fSXeWD9Axw+dZg7L72Tx4c9TmyDWKejnZO/ws+utbuwU212rtyJ3+en/U/bk/xQMj2v70l4g3CnI4r8KHOT57IkfUm1jg21Qpl7xdwaTiQiIoFM5fhH+vLAl/xq9a/4Ku8rLm93OR/d/BGuOJfTsc7p+N7jeBZ7SF+cTmFuIQ1aNGDgzIG4p7iJ7R74pV6kuuIaxTE5afI5V48jQiOYnDSZ1jGtazGdiIgEmhorx5ZltQOWAq0BP/AnY8zzNfV+te1g0UEeWP8AS9KXEBcTx6vXvsrNfW4O6BvUKsoryF6ZjZ1is+vjXQB0GdmFUc+OInF8IqERGtQhdVN1Vo+1aiwiIlCzK8c+YLYxxrYsqxGw2bKsdcaY7TX4njXOW+Hlxa9e5OG/PkyJt4R7Bt/D3OS5NIps5HS0H3Qk+wh2qk3GKxkUHy6mcdvGJM9NxjXZRdOOTZ2OJ1LjzrV6rFVjERH5pxorx8aYfCC/6ucnLcvKAuKBoC3H63evZ/pH09l+eDuju47muVHPkRib6HSsM/IWe9n+7nbsVJt9G/YREhZCt3HdcE9102VUF0JCQ5yOKFKrzrZ6rFVjERH5p1rZc2xZVkfABfzjDK/dCdwJ0L59+9qIc972Ht/L7I9nszxrOZ2bdeaD//iAcd3GBeQWinxPPnaqzdbXt1J2oozmXZsz/MnhJN2WREzrGKfj1QpjDAe+PEDa/DRyVufgLfESHh1OwtgEBs8ZTJv+bQLy905q1g+tHmvVWERETmcZY2r2DSwrBvgMeMIY897Zju3Xr5/ZtGlTjeY5HyXeEp76+1M8+fcnCbFCeHDIg8wePJuosMB6zm9ZYRlb39iKnWqTvzmf0MhQel7fE/dUNx2u6FCvimCFt4IVt64g+8NsfKU+jP//zm8rxCIsOozEcYlMWDqB0HDtsa5v8k/m03lhZ0p9pd99Ljosmt0zdqsci4jUI5ZlbTbG9DvTazW6cmxZVjiwHHj9XMU4kBhjWLFjBbM+nsU3x7/hhl438PSIp2nXpJ3T0b5jjCE3LRc7xSbz7Uy8xV5a9mnJ6IWj6fuLvkQ3i3Y6Yq0zxnxXjL3F3n9/3W/wnvKy44MdrLh1BRPfmFiv/uIg/756rFVjERH5vpp8WoUFLAKyjDELaup9Lrasw1lM/2g6f9n9F/q07MOnt33K0I5DnY71neIjxWQszcBOtTmSdYSImAj63NwH91R3vd8ucODLA2SvPHMxPp2vxEf2ymzyvsojfkB8LaWTQHH63mPtNRYRke+ryZXjy4FbgK2WZaVXfe5BY8zqGnzPszrfMbJHi48GRDE2fsPu9bvxpHrIej8Lv9dP28vaMi51HL1v6E1ETITTEQNC2jNp+Ep81TrWV+Ij7Zk0rn/r+hpOJYHmn6vHL21+SavGIiLyb2ryaRV/AwJqGfN8x8hO6D6hFlL9sMIDhaQvqRznfPyb40Q3j6b//+uPa4qLVn1aOZotEOWsyvmXPcZnY/yGnat21nAiCVRzk+eydtdarRqLiMi/qVcT8oJhjKzf52fnqp14Uj3krK4se52GdWLYvGH0uLYHYVH16rfsvHhLzr6d4vuqu8osdU9cozh2Td/ldAwREQlA9app/fOfU1PtVLz+Hy5STtykU7CrAM8iD+kvp1OUX0RM6xguv+9yXFNcNO/SvNZyBLPw6PBz7jc+XVh0vTr9RUREpBrqVTvw+X3ExcSdtRhD7a0a+0p9ZL2fhSfVw55P9mCFWCSMScA11UW3sd0ICdOgjvORMDaBrOVZ1dpaYYVYdBvbrRZSiYiISDCpN+X40z2fMm3NNDIPZ9KucTu+Lfr2jCW5NlaND207hJ1qs+XVLZQUlNC0Y1OufOxKkn6ZROO2jWvsfeu6QbMHVQ79OHXu1eOwqDAGzR5UC6lEREQkmNT5crzvxD7mfDyHd7a/Q6emnVhxwwr6t+lPlxe6nLEc19SqcXlROdve2oYn1UPuxlxCwkPocW0PXFNddB7eGSskoO5dDErxA+JJHJfIjg92nHU/cVh0GInjE2nTv00tphMREZFgUGfLcamvlPlfzGfehnkAPDr0UeYMnkN0eOVwjNoYI2uMIe+rPOxUm23LtlFeVE5sj1hGPjOSvrf0pWGLhhflfaSSZVlMWDqhchDIymx8JWeYkBdVWYwnLJ1Qr58JLSIiImdW4+Ojz8fFGB9tjOHD7A+5e+3d7Dm+h0k9J/H0iKfp0LTDvxxXk2NkSwpK2PL6FjypHg5uOUhYdBi9b+iN+w43bQe1VSmrYf/8S8kX878gZ3UOvhIfYdFhdBvbjUFzBhHfX4M/RERE6jPHxkfXth1HdjDzo5ms3bWWXi16sf7W9QzrNOyMx17sMbLGGPZ+thc71Wb7u9upKKsg7tI4xv5hLL1v7E1Uk6gf80uT82BZFvED4pn09iSno4iIiEiQqRPluLCskMc+e4zn/vEcDcMb8vzo57mr312Eh4af9esuxhjZooNFpL+cjifVQ8HXBUQ2icQ1xYV7qps4V9wF/XpERERExBlBXY79xs9rW17jvr/cx8Gig9zuup15w+fRsmHLan39hY6R9Vf42bV2F3aqzc6VO/H7/LT/aXuS5ybT8/qehDc4eykXERERkcAUtOV4c95mpq2ZRlpuGgPjB/Lhf3xI//j+5/19zmeM7PG9x/Es9pC+OJ3C3EIatGjAwJkDcU9xE9s99kJ+GSIiIiISQIKuHB8pPsKD6x8k1U6lRcMWLLlmCbdecish1oUNzDjXGNmK8gqyP8zGTrXZ9XHlcV1GdmHUs6NIHJ9IaEToBb2viIiIiASeoCnHPr+PP276I3M/nUtReREzL5vJw1c8TJOoJjXyfkd2HMFeZJPxSgbFh4tp3LYxyXOTcd3uommHpjXyniIiIiLirKAox5998xnT1kxj66GtXNX5Kp4f/Tw9W/S86O/jLfay/d3t2Kk2+zbsIyQshG7juuG+w02XkV0ICdU4ZxEREZG6LKDL8f4T+7ln3T28lfkWHZp0YPnPl3Nt92sv+nOC8z352Kk2W1/fStmJMpp3bc7wJ4eTdFsSMa1jLup7iYiIiEjgCshyXOor5ZkvnmHe3+bhN34eueIR7r383u+m210MZYVlbH1jK3aqTf7mfEIjQ+l5fU/cU910uKKDBnWIiIiI1EMBV45XZq9k5tqZ7D62m+t6XMf8kfPp2LTjRfnexhj2f7EfT6qHzLcz8RZ7admnJaMXjqbvL/oS3ezilW8RERERCT4BVY5zCnIY/+Z4esT2YN0t67iq81UX5fueOnyKLa9uwU61OZJ1hIiYCPrc3Af3HW7a9GujVWIRERERAQKsHBeVF/HsqGf57/7/fc7pdudi/Ibd63fjSfWQ9X4Wfq+ftpe1Zfyi8fT6eS8iYiIuUmoRERERqSssY4zTGb5zifsSk2Fn/KjvUZhbWDnOeZGH498cJ7p5NH1v6Yt7qpuWvas3OU9ERERE6i7LsjYbY/qd6bWAWjkOD7mw1WK/z8/OVTvxpHrIWZ2D8Rs6DevEsHnD6HFtD8KiAuqXKSIiIiIBKqhbY8GuAjyLPKS/nE5RfhExrWO4/L7LcU1x0bxLc6fjiYiIiEiQCbpy7Cv1kfV+Fp5UD3s+2YMVYpEwJgH3HW4SxiQQEqZBHSIiIiJyYYKmHB/adgg71WbLq1soKSihacemXPnYlSRNTqJxfGOn44mIiIhIHRDQ5bi8qJxtb23Dk+ohd2MuIeEh9Li2B+473HQa1gkrpH49gs0Yw4EvD5A2P42c1Tl4S7yER4eTMDaBwXMG06a/HksnIiIi8mMEXDk2xpD3VR52qs22ZdsoLyontkcsI58ZySW3XkKD2AZOR3REhbeCFbeuIPvDbHylPoy/8ikj3mIvWcuzyFmdQ+K4RCYsnUBoeKjDaUVERESCU0A9yq1H+x5metPpHNp6iLDoMHrf0Bv3HW7aDmpbr1dEjTG8d9N7ZH+YjbfY+4PHhUWH0f2a7kx8Y2K9/u8lIiIicjZB8yi3E/tPENoylLF/GEvvG3sT1STK6UgB4cCXB8heefZiDOAr8ZG9Mpu8r/KIHxBfS+lERERE6o6AerRDix4tuHPTnfT7r34qxqdJeyYNX4mvWsf6SnykPZNWw4lERERE6qaAKsfhDX7cyOi6KmdVznd7jM/F+A07V+2s4UQiIiIidVNAlWM5M2/J2bdTfF91V5lFRERE5F+pHAeB8OjzW1EPiw6oreQiIiIiQUPlOAgkjE2o9jOdrRCLbmO71XAiERERkbpJ5TgIDJo9qNqrwWFRYQyaPaiGE4mIiIjUTSrHQSB+QDyJ4xLPWZDDosNIHJ9Im/5taimZiIiISN2ichwELMtiwtIJdL+mO+ENw/9ti4UVYhHeIJzu13RnwtIJGgAiIiIicoF051aQCA0PZeIbE8n7Ko8v5n9BzuocfCU+wqLD6Da2G4PmDCK+vwZ/iIiIiPwYKsdBxLIs4gfEM+ntSU5HEREREamTtK1CRERERKSKyrGIiIiISBWVYxERERGRKirHIiIiIiJVVI5FRERERKqoHIuIiIiIVFE5FhERERGponIsIiIiIlJF5VhEREREpIrKsYiIiIhIFZVjEREREZEqKsciIiIiIlUsY4zTGb5jWdZhYK/TOeqBWOCI0yEkoOkckbPR+SHnonNEziYQzo8OxpgWZ3ohoMqx1A7LsjYZY/o5nUMCl84RORudH3IuOkfkbAL9/NC2ChERERGRKirHIiIiIiJVVI7rpz85HUACns4RORudH3IuOkfkbAL6/NCeYxERERGRKlo5FhERERGponJch1mWNdqyrGzLsr62LOv+M7w+1LKsE5ZlpVf9+I0TOcUZlmUttizrkGVZ237gdcuyrIVV588Wy7LctZ1RnFON80PXj3rMsqx2lmV9allWlmVZmZZlzTjDMbqG1GPVPEcC8joS5nQAqRmWZYUCLwIjgFzgK8uyPjTGbP/eoRuMMVfXekAJBC8D/wss/YHXfwYkVP0YCPyh6n+lfniZs58foOtHfeYDZhtjbMuyGgGbLcta970/Y3QNqd+qc45AAF5HtHJcdw0AvjbG7DbGlANvAtc4nEkCiDHmc6DgLIdcAyw1lTYCTS3LiquddOK0apwfUo8ZY/KNMXbVz08CWUD89w7TNaQeq+Y5EpBUjuuueGD/aR/ncuaTcpBlWRmWZa2xLKtX7USTIFHdc0jqL10/BMuyOgIu4B/fe0nXEAHOeo5AAF5HtK2i7rLO8LnvP5rEpnJ8YpFlWWOAFVT+85cIVO8ckvpL1w/BsqwYYDkw0xhT+P2Xz/AluobUM+c4RwLyOqKV47orF2h32sdtgbzTDzDGFBpjiqp+vhoItywrtvYiSoA75zkk9ZeuH2JZVjiVped1Y8x7ZzhE15B67lznSKBeR1SO666vgATLsjpZlhUB/Afw4ekHWJbV2rIsq+rnA6g8H47WelIJVB8Ct1bdcX4ZcMIYk+90KAkMun7Ub1W/94uALGPMgh84TNeQeqw650igXke0raKOMsb4LMv6FbAWCAUWG2MyLcv6r6rX/whcD9xlWZYPKAH+w2gqTL1hWdYyYCgQa1lWLvAwEA7fnR+rgTHA10AxMNmZpOKEapwfun7Ub5cDtwBbLctKr/rcg0B70DVEgOqdIwF5HdGEPBERERGRKtpWISIiIiJSReVYRERERKSKyrGIiIiISBWVYxERERGRKirHIiIiIiJV9Cg3EZEAYlnWT4D1VR+2BiqAw1UfXwJknHb4m8aYJy3Luhp4jMoFj3DgeSAWmFR1XB9ga9XPFxtjFtbcr0BEJLjpUW4iIgHKsqxHgCJjzPyqj4uMMTHfOyYc2AsMMMbkWpYVCXQ0xmSfdsy/fZ2IiJyZVo5FRIJbIyqv5UcBjDFlQPZZv0JERH6Q9hyLiASPaMuy0k/7cYMxpoDKMb17LctaZlnWzZZl6douInKBtHIsIhI8SowxSd//pDFmqmVZfYCrgDnACOCXtZxNRKRO0OqCiEgdYIzZaox5lspifJ3TeUREgpXKsYhIELMsK8ayrKGnfSqJyhv0RETkAmhbhYhI8Ii2LCv9tI8/Ap4A7rUs6yWgBDiFtlSIiFwwPcpNRERERKSKtlWIiIiIiFRRORYRERERqaJyLCIiIiJSReVYRERERKSKyrGIiIiISBWVYxERERGRKirHIiIiIiJVVI5FRERERKr8f2BkmyFhkcL5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111, xlabel='TEST', ylabel='JPERF')\n",
    "for factor, group in factor_group:\n",
    "    ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
    "                marker=markers[factor], s=12**2)\n",
    "\n",
    "fig = abline_plot(intercept = min_lm2.params['Intercept'],\n",
    "                 slope = min_lm2.params['TEST'], ax=ax, color='purple')\n",
    "ax = fig.axes[0]\n",
    "fig = abline_plot(intercept = min_lm2.params['Intercept'],\n",
    "        slope = min_lm2.params['TEST'] + min_lm2.params['TEST:ETHN'],\n",
    "        ax=ax, color='green')\n",
    "ax.legend(['ETHN == 1', 'ETHN == 0'], scatterpoints=1, loc='upper left');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  JPERF   R-squared:                       0.572\n",
      "Model:                            OLS   Adj. R-squared:                  0.522\n",
      "Method:                 Least Squares   F-statistic:                     11.38\n",
      "Date:                Tue, 14 Apr 2020   Prob (F-statistic):           0.000731\n",
      "Time:                        22:11:04   Log-Likelihood:                -35.390\n",
      "No. Observations:                  20   AIC:                             76.78\n",
      "Df Residuals:                      17   BIC:                             79.77\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.6120      0.887      0.690      0.500      -1.260       2.483\n",
      "TEST           2.2988      0.522      4.400      0.000       1.197       3.401\n",
      "ETHN           1.0276      0.691      1.487      0.155      -0.430       2.485\n",
      "==============================================================================\n",
      "Omnibus:                        0.251   Durbin-Watson:                   3.028\n",
      "Prob(Omnibus):                  0.882   Jarque-Bera (JB):                0.437\n",
      "Skew:                          -0.059   Prob(JB):                        0.804\n",
      "Kurtosis:                       2.286   Cond. No.                         5.72\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "min_lm3 = ols('JPERF ~ TEST + ETHN', data=minority_table).fit()\n",
    "print(min_lm3.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111, xlabel='TEST', ylabel='JPERF')\n",
    "for factor, group in factor_group:\n",
    "    ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
    "                marker=markers[factor], s=12**2)\n",
    "\n",
    "fig = abline_plot(intercept = min_lm3.params['Intercept'],\n",
    "                 slope = min_lm3.params['TEST'], ax=ax, color='purple')\n",
    "\n",
    "ax = fig.axes[0]\n",
    "fig = abline_plot(intercept = min_lm3.params['Intercept'] + min_lm3.params['ETHN'],\n",
    "        slope = min_lm3.params['TEST'], ax=ax, color='green')\n",
    "ax.legend(['ETHN == 1', 'ETHN == 0'], scatterpoints=1, loc='upper left');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  JPERF   R-squared:                       0.664\n",
      "Model:                            OLS   Adj. R-squared:                  0.601\n",
      "Method:                 Least Squares   F-statistic:                     10.55\n",
      "Date:                Tue, 14 Apr 2020   Prob (F-statistic):           0.000451\n",
      "Time:                        22:11:04   Log-Likelihood:                -32.971\n",
      "No. Observations:                  20   AIC:                             73.94\n",
      "Df Residuals:                      16   BIC:                             77.92\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      2.0103      1.050      1.914      0.074      -0.216       4.236\n",
      "TEST           1.3134      0.670      1.959      0.068      -0.108       2.735\n",
      "ETHN          -1.9132      1.540     -1.242      0.232      -5.179       1.352\n",
      "TEST:ETHN      1.9975      0.954      2.093      0.053      -0.026       4.021\n",
      "==============================================================================\n",
      "Omnibus:                        3.377   Durbin-Watson:                   3.015\n",
      "Prob(Omnibus):                  0.185   Jarque-Bera (JB):                1.330\n",
      "Skew:                           0.120   Prob(JB):                        0.514\n",
      "Kurtosis:                       1.760   Cond. No.                         13.8\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "min_lm4 = ols('JPERF ~ TEST * ETHN', data=minority_table).fit()\n",
    "print(min_lm4.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111, ylabel='JPERF', xlabel='TEST')\n",
    "for factor, group in factor_group:\n",
    "    ax.scatter(group['TEST'], group['JPERF'], color=colors[factor],\n",
    "                marker=markers[factor], s=12**2)\n",
    "\n",
    "fig = abline_plot(intercept = min_lm4.params['Intercept'],\n",
    "                 slope = min_lm4.params['TEST'], ax=ax, color='purple')\n",
    "ax = fig.axes[0]\n",
    "fig = abline_plot(intercept = min_lm4.params['Intercept'] + min_lm4.params['ETHN'],\n",
    "        slope = min_lm4.params['TEST'] + min_lm4.params['TEST:ETHN'],\n",
    "        ax=ax, color='green')\n",
    "ax.legend(['ETHN == 1', 'ETHN == 0'], scatterpoints=1, loc='upper left');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Is there any effect of ETHN on slope or intercept?\n",
    "<br />\n",
    "Y ~ TEST vs. Y ~ TEST + ETHN + ETHN:TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid        ssr  df_diff    ss_diff         F    Pr(>F)\n",
      "0      18.0  45.568297      0.0        NaN       NaN       NaN\n",
      "1      16.0  31.655473      2.0  13.912824  3.516061  0.054236\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in greater\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in less\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1912: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= _a)\n"
     ]
    }
   ],
   "source": [
    "table5 = anova_lm(min_lm, min_lm4)\n",
    "print(table5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Is there any effect of ETHN on intercept?\n",
    "<br />\n",
    "Y ~ TEST vs. Y ~ TEST + ETHN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid        ssr  df_diff   ss_diff         F    Pr(>F)\n",
      "0      18.0  45.568297      0.0       NaN       NaN       NaN\n",
      "1      17.0  40.321546      1.0  5.246751  2.212087  0.155246\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in greater\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in less\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1912: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= _a)\n"
     ]
    }
   ],
   "source": [
    "table6 = anova_lm(min_lm, min_lm3)\n",
    "print(table6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Is there any effect of ETHN on slope?\n",
    "<br />\n",
    "Y ~ TEST vs. Y ~ TEST + ETHN:TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid        ssr  df_diff    ss_diff         F    Pr(>F)\n",
      "0      18.0  45.568297      0.0        NaN       NaN       NaN\n",
      "1      17.0  34.707653      1.0  10.860644  5.319603  0.033949\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in greater\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in less\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1912: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= _a)\n"
     ]
    }
   ],
   "source": [
    "table7 = anova_lm(min_lm, min_lm2)\n",
    "print(table7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Is it just the slope or both?\n",
    "<br />\n",
    "Y ~ TEST + ETHN:TEST vs Y ~ TEST + ETHN + ETHN:TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid        ssr  df_diff  ss_diff         F    Pr(>F)\n",
      "0      17.0  34.707653      0.0      NaN       NaN       NaN\n",
      "1      16.0  31.655473      1.0  3.05218  1.542699  0.232115\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in greater\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in less\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Programs\\WPy64-3760\\python-3.7.6.amd64\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1912: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= _a)\n"
     ]
    }
   ],
   "source": [
    "table8 = anova_lm(min_lm2, min_lm4)\n",
    "print(table8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Two Way ANOVA - Kidney failure data"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Weight - (1,2,3) - Level of weight gan between treatments\n",
    "Duration - (1,2) - Level of duration of treatment\n",
    "Days - Time of stay in hospital"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kidney.table read directly\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    kidney_table = pd.read_csv('kidney.table', delim_whitespace=True)\n",
    "    print('kidney.table read directly')\n",
    "except:\n",
    "    url = 'http://stats191.stanford.edu/data/kidney.table'\n",
    "    kidney_table = pd.read_table(url, delim_whitespace=True)\n",
    "    kidney_table.to_csv(\"kidney.table\", sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weight  Duration\n",
      "1       1           10\n",
      "        2           10\n",
      "2       1           10\n",
      "        2           10\n",
      "3       1           10\n",
      "        2           10\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Explore the dataset, it's a balanced design\n",
    "print(kidney_table.groupby(['Weight', 'Duration']).size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kt = kidney_table\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "ax = fig.add_subplot(111)\n",
    "fig = interaction_plot(kt['Weight'], kt['Duration'], np.log(kt['Days']+1),\n",
    "        colors=['red', 'blue'], markers=['D','^'], ms=10, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "$$Y_{ijk} = \\mu + \\alpha_i + \\beta_j + \\left(\\alpha\\beta\\right)_{ij}+\\epsilon_{ijk}$$\n",
    "\n",
    "with \n",
    "\n",
    "$$\\epsilon_{ijk}\\sim N\\left(0,\\sigma^2\\right)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function anova_lm in module statsmodels.stats.anova:\n",
      "\n",
      "anova_lm(*args, **kwargs)\n",
      "    Anova table for one or more fitted linear models.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    args : fitted linear model results instance\n",
      "        One or more fitted linear models\n",
      "    scale : float\n",
      "        Estimate of variance, If None, will be estimated from the largest\n",
      "        model. Default is None.\n",
      "    test : str {\"F\", \"Chisq\", \"Cp\"} or None\n",
      "        Test statistics to provide. Default is \"F\".\n",
      "    typ : str or int {\"I\",\"II\",\"III\"} or {1,2,3}\n",
      "        The type of Anova test to perform. See notes.\n",
      "    robust : {None, \"hc0\", \"hc1\", \"hc2\", \"hc3\"}\n",
      "        Use heteroscedasticity-corrected coefficient covariance matrix.\n",
      "        If robust covariance is desired, it is recommended to use `hc3`.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    anova : DataFrame\n",
      "        When args is a single model, return is DataFrame with columns:\n",
      "    \n",
      "        sum_sq : float64\n",
      "            Sum of squares for model terms.\n",
      "        df : float64\n",
      "            Degrees of freedom for model terms.\n",
      "        F : float64\n",
      "            F statistic value for significance of adding model terms.\n",
      "        PR(>F) : float64\n",
      "            P-value for significance of adding model terms.\n",
      "    \n",
      "        When args is multiple models, return is DataFrame with columns:\n",
      "    \n",
      "        df_resid : float64\n",
      "            Degrees of freedom of residuals in models.\n",
      "        ssr : float64\n",
      "            Sum of squares of residuals in models.\n",
      "        df_diff : float64\n",
      "            Degrees of freedom difference from previous model in args\n",
      "        ss_dff : float64\n",
      "            Difference in ssr from previous model in args\n",
      "        F : float64\n",
      "            F statistic comparing to previous model in args\n",
      "        PR(>F): float64\n",
      "            P-value for significance comparing to previous model in args\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    Model statistics are given in the order of args. Models must have been fit\n",
      "    using the formula api.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    model_results.compare_f_test, model_results.compare_lm_test\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> import statsmodels.api as sm\n",
      "    >>> from statsmodels.formula.api import ols\n",
      "    >>> moore = sm.datasets.get_rdataset(\"Moore\", \"carData\", cache=True) # load\n",
      "    >>> data = moore.data\n",
      "    >>> data = data.rename(columns={\"partner.status\" :\n",
      "    ...                             \"partner_status\"}) # make name pythonic\n",
      "    >>> moore_lm = ols('conformity ~ C(fcategory, Sum)*C(partner_status, Sum)',\n",
      "    ...                 data=data).fit()\n",
      "    >>> table = sm.stats.anova_lm(moore_lm, typ=2) # Type 2 Anova DataFrame\n",
      "    >>> print(table)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(anova_lm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Things available in the calling namespace are available in the formula evaluation namespace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "kidney_lm = ols('np.log(Days+1) ~ C(Duration) * C(Weight)', data=kt).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "ANOVA Type-I Sum of Squares\n",
    "<br /><br />\n",
    "SS(A) for factor A. <br />\n",
    "SS(B|A) for factor B. <br />\n",
    "SS(AB|B, A) for interaction AB. <br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         df     sum_sq   mean_sq          F    PR(>F)\n",
      "C(Duration)             1.0   2.339693  2.339693   4.358293  0.041562\n",
      "C(Weight)               2.0  16.971291  8.485645  15.806745  0.000004\n",
      "C(Duration):C(Weight)   2.0   0.635658  0.317829   0.592040  0.556748\n",
      "Residual               54.0  28.989198  0.536837        NaN       NaN\n"
     ]
    }
   ],
   "source": [
    "print(anova_lm(kidney_lm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "ANOVA Type-II Sum of Squares\n",
    "<br /><br />\n",
    "SS(A|B) for factor A. <br />\n",
    "SS(B|A) for factor B. <br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          sum_sq    df          F    PR(>F)\n",
      "C(Duration)             2.339693   1.0   4.358293  0.041562\n",
      "C(Weight)              16.971291   2.0  15.806745  0.000004\n",
      "C(Duration):C(Weight)   0.635658   2.0   0.592040  0.556748\n",
      "Residual               28.989198  54.0        NaN       NaN\n"
     ]
    }
   ],
   "source": [
    "print(anova_lm(kidney_lm, typ=2))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "ANOVA Type-III Sum of Squares\n",
    "<br /><br />\n",
    "SS(A|B, AB) for factor A. <br />\n",
    "SS(B|A, AB) for factor B. <br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                      sum_sq    df           F        PR(>F)\n",
      "Intercept                         156.301830   1.0  291.153237  2.077589e-23\n",
      "C(Duration, Sum)                    2.339693   1.0    4.358293  4.156170e-02\n",
      "C(Weight, Poly)                    16.971291   2.0   15.806745  3.944502e-06\n",
      "C(Duration, Sum):C(Weight, Poly)    0.635658   2.0    0.592040  5.567479e-01\n",
      "Residual                           28.989198  54.0         NaN           NaN\n"
     ]
    }
   ],
   "source": [
    "print(anova_lm(ols('np.log(Days+1) ~ C(Duration, Sum) * C(Weight, Poly)', \n",
    "                   data=kt).fit(), typ=3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Excercise: Find the 'best' model for the kidney failure dataset"
   ]
  }
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